GSNAP: Fast and SNP-tolerant detection of complex variants and splicing in short reads by Thomas D. Wu and Serban Nacu
|
|
|
- Meagan Burns
- 5 years ago
- Views:
Transcription
1 GSNAP: Fast and SNP-tolerant detection of complex variants and splicing in short reads by Thomas D. Wu and Serban Nacu Matt Huska Freie Universität Berlin Computational Methods for High-Throughput Omics Data, WS 2011
2 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection FU Berlin, GSNAP, Omics WS
3 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions FU Berlin, GSNAP, Omics WS
4 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations FU Berlin, GSNAP, Omics WS
5 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
6 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
7 Motivation New technology allows us to sequence more reads in shorter time. Increasing at an incredible rate with no signs of slowing down. FU Berlin, GSNAP, Omics WS
8 Motivation New technology allows us to sequence more reads in shorter time. Increasing at an incredible rate with no signs of slowing down. "Why should we be happy with millions of reads, when we can have... FU Berlin, GSNAP, Omics WS
9 Motivation...billions?" FU Berlin, GSNAP, Omics WS
10 Motivation: Why do we need another read mapping algorithm? The reads themselves are also getting longer. Longer reads = higher probability for complex variants within a read. FU Berlin, GSNAP, Omics WS
11 Motivation: Why do we need another read mapping algorithm? The reads themselves are also getting longer. Longer reads = higher probability for complex variants within a read. Current (Feb 2010) read mappers tend to either be very fast (BWA, Bowtie, SOAP2) or sensitive to variants (SOAP) FU Berlin, GSNAP, Omics WS
12 Motivation: Why do we need another read mapping algorithm? The reads themselves are also getting longer. Longer reads = higher probability for complex variants within a read. Current (Feb 2010) read mappers tend to either be very fast (BWA, Bowtie, SOAP2) or sensitive to variants (SOAP) GSNAP is intended to be fast and able to handle complex variants FU Berlin, GSNAP, Omics WS
13 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
14 Key Features of GSNAP Can handle short and long insertions and deletions FU Berlin, GSNAP, Omics WS
15 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) FU Berlin, GSNAP, Omics WS
16 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) user-provided database of splice sites (eg. RefSeq) FU Berlin, GSNAP, Omics WS
17 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) user-provided database of splice sites (eg. RefSeq) probabilistic model FU Berlin, GSNAP, Omics WS
18 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) user-provided database of splice sites (eg. RefSeq) probabilistic model SNP tolerant (given a user-provided database eg. dbsnp) FU Berlin, GSNAP, Omics WS
19 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) user-provided database of splice sites (eg. RefSeq) probabilistic model SNP tolerant (given a user-provided database eg. dbsnp) Can align bisulfite-treated DNA (for studying methlyation state) FU Berlin, GSNAP, Omics WS
20 Key Features of GSNAP Can handle short and long insertions and deletions Detects short and long distance splicing (including interchromosomal) user-provided database of splice sites (eg. RefSeq) probabilistic model SNP tolerant (given a user-provided database eg. dbsnp) Can align bisulfite-treated DNA (for studying methlyation state) Still pretty fast FU Berlin, GSNAP, Omics WS
21 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
22 Long deletion of 17nt plus mismatches 17nt deletion matching an entry in dbsnp, including mismatches: FU Berlin, GSNAP, Omics WS
23 Splicing identified using the probabilistic model An intron within exon 1 of HOXA9. Is also experimentally supported. FU Berlin, GSNAP, Omics WS
24 Splicing identified using a database of known splice sites Splicing sites identified despite having low probabilistic scores. FU Berlin, GSNAP, Omics WS
25 Interchromosomal splicing (gene fusion) Splicing between BCAS4 (chr 20) and BCAS3 (chr 17). FU Berlin, GSNAP, Omics WS
26 SNP-tolerant alignment SNP-tolerance allows both genotypes to align well FU Berlin, GSNAP, Omics WS
27 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
28 The Algorithm in Brief Index the genome using a hash table FU Berlin, GSNAP, Omics WS ,
29 The Algorithm in Brief Index the genome using a hash table Break up short reads into shorter elements and look each up in hash table FU Berlin, GSNAP, Omics WS
30 The Algorithm in Brief Index the genome using a hash table Break up short reads into shorter elements and look each up in hash table Look at resulting position lists for each element and see if they support a common target location and have a reasonable number of mismatches FU Berlin, GSNAP, Omics WS
31 The Algorithm in Brief Index the genome using a hash table Break up short reads into shorter elements and look each up in hash table Look at resulting position lists for each element and see if they support a common target location and have a reasonable number of mismatches Verify the number of mismatches by checking the whole read against the reference FU Berlin, GSNAP, Omics WS
32 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
33 Hash Table Space Requirements Hashing every overlapping oligomer in the entire reference sequence would take too much memory (approx. 14GB for the human genome) FU Berlin, GSNAP, Omics WS
34 Hash Table Space Requirements Hashing every overlapping oligomer in the entire reference sequence would take too much memory (approx. 14GB for the human genome) We only hash 12mers every 3nt (approx. 4GB) FU Berlin, GSNAP, Omics WS
35 Hash Table Space Requirements Hashing every overlapping oligomer in the entire reference sequence would take too much memory (approx. 14GB for the human genome) We only hash 12mers every 3nt (approx. 4GB) Optional SNP-tolerance only adds a small amount to total memory requirements (3.8 GB 4.0 GB) FU Berlin, GSNAP, Omics WS
36 Hash Table Space Requirements Hashing every overlapping oligomer in the entire reference sequence would take too much memory (approx. 14GB for the human genome) We only hash 12mers every 3nt (approx. 4GB) Optional SNP-tolerance only adds a small amount to total memory requirements (3.8 GB 4.0 GB) Entire table only needs to be in memory during construction. Afterwards it is mmap d and only part is loaded into memory. FU Berlin, GSNAP, Omics WS
37 Hashing the Reference Genome FU Berlin, GSNAP, Omics WS
38 Including SNPs FU Berlin, GSNAP, Omics WS
39 Resulting Reference "Space" FU Berlin, GSNAP, Omics WS
40 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
41 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
42 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
43 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
44 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
45 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
46 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
47 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
48 Spanning Set Generation and Filtering Generating elements: used to find supporting candidate locations Uses a multiway merging procedure (Knuth TAOCP Vol 3) Slow: linear on the sum of the list lengths. O(l log n) runtime where n is the number of position lists and l is the sum of their lengths. FU Berlin, GSNAP, Omics WS
49 Spanning Set Generation and Filtering Generating elements: used to find supporting candidate locations Uses a multiway merging procedure (Knuth TAOCP Vol 3) Slow: linear on the sum of the list lengths. O(l log n) runtime where n is the number of position lists and l is the sum of their lengths. Filtering elements: filter the candidate locations found using the generating elements Fast: uses a binary search. O(log l i ) for position list i FU Berlin, GSNAP, Omics WS
50 Spanning Set Generation and Filtering Generating elements: used to find supporting candidate locations Uses a multiway merging procedure (Knuth TAOCP Vol 3) Slow: linear on the sum of the list lengths. O(l log n) runtime where n is the number of position lists and l is the sum of their lengths. Filtering elements: filter the candidate locations found using the generating elements Fast: uses a binary search. O(log l i ) for position list i Choose the elements with the shortest position lists as generating elements, and the longer ones as filtering elements. FU Berlin, GSNAP, Omics WS
51 Spanning Set Generation and Filtering Generating elements: used to find supporting candidate locations Uses a multiway merging procedure (Knuth TAOCP Vol 3) Slow: linear on the sum of the list lengths. O(l log n) runtime where n is the number of position lists and l is the sum of their lengths. Filtering elements: filter the candidate locations found using the generating elements Fast: uses a binary search. O(log l i ) for position list i Choose the elements with the shortest position lists as generating elements, and the longer ones as filtering elements. They choose K + 2 generating sets, where K is the constraint score (= max number of mismatches) FU Berlin, GSNAP, Omics WS
52 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
53 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
54 Spanning Set Generation and Filtering FU Berlin, GSNAP, Omics WS
55 Spanning Set Generation and Filtering Problem: The spanning set method can only be used to detect a limited number of mismatches. FU Berlin, GSNAP, Omics WS
56 Spanning Set Generation and Filtering Problem: The spanning set method can only be used to detect a limited number of mismatches. If we want to allow K matches, for K > 1, then we need at least (K + 2) generating elements. The read length L limits the number of elements N, and the spanning set elements are non-overlapping in all three shifts if L = 10(mod12), so N (L + 2)/12. FU Berlin, GSNAP, Omics WS
57 Spanning Set Generation and Filtering Problem: The spanning set method can only be used to detect a limited number of mismatches. If we want to allow K matches, for K > 1, then we need at least (K + 2) generating elements. The read length L limits the number of elements N, and the spanning set elements are non-overlapping in all three shifts if L = 10(mod12), so N (L + 2)/12. So we need to satisfy N > (K + 2), and as such we can only apply the spanning set method when K (L + 2)/12 2 for L 34. FU Berlin, GSNAP, Omics WS
58 Spanning Set Generation and Filtering Problem: The spanning set method can only be used to detect a limited number of mismatches. If we want to allow K matches, for K > 1, then we need at least (K + 2) generating elements. The read length L limits the number of elements N, and the spanning set elements are non-overlapping in all three shifts if L = 10(mod12), so N (L + 2)/12. So we need to satisfy N > (K + 2), and as such we can only apply the spanning set method when K (L + 2)/12 2 for L 34. For reads of length 100 (Illumina), the we can allow a maximum of 6 mismatches. For reads of length 400 (454), the we can allow a maximum of 31 mismatches. FU Berlin, GSNAP, Omics WS
59 Spanning Set Generation and Filtering Problem: The spanning set method can only be used to detect a limited number of mismatches. If we want to allow K matches, for K > 1, then we need at least (K + 2) generating elements. The read length L limits the number of elements N, and the spanning set elements are non-overlapping in all three shifts if L = 10(mod12), so N (L + 2)/12. So we need to satisfy N > (K + 2), and as such we can only apply the spanning set method when K (L + 2)/12 2 for L 34. For reads of length 100 (Illumina), the we can allow a maximum of 6 mismatches. For reads of length 400 (454), the we can allow a maximum of 31 mismatches. If we want to allow larger numbers of mismatches or the same number of mismatches in shorter reads, we need to use another method... FU Berlin, GSNAP, Omics WS
60 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
61 Complete set generation and filtering Uses the complete set of overlapping 12mers. Works for any number of mismatches as long as read and target have 14 consecutive matches (12mer out of phase by up to two bases) Exhaustive for K L/14 1 FU Berlin, GSNAP, Omics WS
62 Complete set generation and filtering FU Berlin, GSNAP, Omics WS
63 Complete set generation and filtering Lower bound on mismatches: ( p + 6)/12 where p is the distance between start locations of consecutive supporting 12mers. FU Berlin, GSNAP, Omics WS
64 Complete set example FU Berlin, GSNAP, Omics WS
65 Complete set generation and filtering FU Berlin, GSNAP, Omics WS
66 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
67 Verification of Candidate Regions The spanning set and complete set methods generate candidate regions for which we know a lower bound on the number of mismatches. These regions need to be verified to check the exact number of mismatches. FU Berlin, GSNAP, Omics WS
68 Remember: Resulting Reference "Space" FU Berlin, GSNAP, Omics WS
69 Compressed Genome Text usually stored as 8 bit characters FU Berlin, GSNAP, Omics WS ,
70 Compressed Genome Text usually stored as 8 bit characters Because we have a reduced alphabet the reference is stored as 3 bits per character: 2 bits for the nt + a flag FU Berlin, GSNAP, Omics WS ,
71 Compressed Genome Text usually stored as 8 bit characters Because we have a reduced alphabet the reference is stored as 3 bits per character: 2 bits for the nt + a flag Flag in major-allele genome: indicates unknown or ambiguous nt Flag in minor-allele genome: indicates a SNP FU Berlin, GSNAP, Omics WS ,
72 Verification of Candidate Regions Query sequence converted to the same compressed representation as the reference Shifted into position and bitwise XOR combined with the major- and minor-allele genomes separately Resulting arrays are bitwise AND d, so mismatches at a SNP only occur if both alleles do not match FU Berlin, GSNAP, Omics WS
73 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
74 Detecting Insertions and Deletions FU Berlin, GSNAP, Omics WS
75 Detecting Insertions and Deletions FU Berlin, GSNAP, Omics WS
76 Detecting Insertions and Deletions FU Berlin, GSNAP, Omics WS
77 Detecting Insertions and Deletions FU Berlin, GSNAP, Omics WS
78 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
79 Detecting Splice Junctions Known splice sites: user-provided database Novel splice sites: maximum entropy probabilistic model from Yeo and Burge, 2004 FU Berlin, GSNAP, Omics WS
80 Detecting Splice Junctions Short-distance splice sites are on the same chromosome and < some distance apart (default: 200,000 nt) Method similar to the one we used to find middle deletions earlier... FU Berlin, GSNAP, Omics WS
81 Detecting Splice Junctions FU Berlin, GSNAP, Omics WS
82 Detecting Splice Junctions Crossover area is then searched for donor or acceptor sites (either known or novel with high probability). FU Berlin, GSNAP, Omics WS
83 Detecting Splice Junctions Long-distance splice sites can be on different chromosomes Require higher probability scores for novel splice sites than short-distance splice sites Candidates with matching breakpoints on the read are matched FU Berlin, GSNAP, Omics WS
84 Detecting Splice Junctions FU Berlin, GSNAP, Omics WS
85 Detecting Splice Junctions If both splice sites can not be found then GSNAP will return one site (a "half-intron") FU Berlin, GSNAP, Omics WS
86 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
87 Simulated Reads Runtime comparison between GSNAP and other alignment tools (for 100,000 reads) Simulated increasingly complicated variants exact matches only 1-3 mismatches short insertions and deletions longer insertions and deletions FU Berlin, GSNAP, Omics WS
88 Simulated Reads: 36nt Variant: Exact Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
89 Simulated Reads: 36nt Variant: 1 mm Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
90 Simulated Reads: 36nt Variant: 2 mm Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
91 Simulated Reads: 36nt Variant: 3 mm Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
92 Simulated Reads: 36nt Variant: Ins (1...3nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
93 Simulated Reads: 36nt Variant: Del (1...3nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
94 Simulated Reads: 36nt Variant: Ins (4...9nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
95 Simulated Reads: 36nt Variant: Del (4...30nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ Mapping Software FU Berlin, GSNAP, Omics WS
96 Simulated Reads: 36nt Exact 1 mm 2 mm 3 mm Ins (1...3nt) Del (1...3nt) Ins (4...9nt) Del (4...30nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ FU Berlin, GSNAP, Omics WS
97 Simulated Reads: 70nt Exact 1 mm 2 mm 3 mm 4 mm Ins (1...3nt) Del (1...3nt) Ins (4...9nt) Del (4...30nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ FU Berlin, GSNAP, Omics WS
98 Simulated Reads: 100nt Exact 1 mm 2 mm 3 mm 4 mm 5 mm Ins (1...3nt) Del (1...3nt) Ins (4...9nt) Del (4...30nt) Runtime (s) GSNAP BWA Bowtie SOAP2 SOAP MAQ FU Berlin, GSNAP, Omics WS
99 Simulated Reads: Percent misses (on unique reads) Most algorithms were able to perfectly map unique reads, with the following notable exceptions: FU Berlin, GSNAP, Omics WS
100 Simulated Reads: Percent misses (on unique reads) Most algorithms were able to perfectly map unique reads, with the following notable exceptions: GSNAP: 12% misses for 36nt reads with 3 mismatches FU Berlin, GSNAP, Omics WS
101 Simulated Reads: Percent misses (on unique reads) Most algorithms were able to perfectly map unique reads, with the following notable exceptions: GSNAP: 12% misses for 36nt reads with 3 mismatches SOAP: 15% misses for 36nt reads with 3 mismatches, around 5% for 1-3nt indels FU Berlin, GSNAP, Omics WS
102 Simulated Reads: Percent misses (on unique reads) Most algorithms were able to perfectly map unique reads, with the following notable exceptions: GSNAP: 12% misses for 36nt reads with 3 mismatches SOAP: 15% misses for 36nt reads with 3 mismatches, around 5% for 1-3nt indels BWA: 1-5% misses in 1-3nt indels FU Berlin, GSNAP, Omics WS
103 Memory Requirements GSNAP: should have access to 5 GB of memory, otherwise it will run slowly BWA: 2.2 GB Bowtie: 1.1 GB (exact matches) or 2.2 GB (allowing mismatches) MAQ: 302 MB SOAP: 14 GB SOAP2: unknown ("only provided as a binary and did not have the required compile time flag") FU Berlin, GSNAP, Omics WS
104 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
105 Transcriptional Reads Only looked at the effect of splicing, indels and SNP tolerance FU Berlin, GSNAP, Omics WS
106 Transcriptional Reads Only looked at the effect of splicing, indels and SNP tolerance Including known splicing information increased yield approx. 8% FU Berlin, GSNAP, Omics WS
107 Transcriptional Reads Only looked at the effect of splicing, indels and SNP tolerance Including known splicing information increased yield approx. 8% Including SNP tolerance minor increase in yield (0.5%) but effected about 8% of alignments FU Berlin, GSNAP, Omics WS
108 Outline Introduction Motivation GSNAP Features Examples of Complex Variant Detection The Algorithm Summary Preprocessing Method 1: Spanning Set Generation and Filtering Method 2: Complete Set Generation and Filtering Verification of Candidate Regions Detecting Insertions and Deletions Detecting Splice Junctions Results Simulated Reads Transcriptional Reads Limitations Conclusions FU Berlin, GSNAP, Omics WS
109 Limitations Matching is only exhaustive if there is at least a 14nt contiguous match, otherwise it s a heuristic FU Berlin, GSNAP, Omics WS
110 Limitations Matching is only exhaustive if there is at least a 14nt contiguous match, otherwise it s a heuristic Limited to one indel or splice site per read FU Berlin, GSNAP, Omics WS
111 Limitations Matching is only exhaustive if there is at least a 14nt contiguous match, otherwise it s a heuristic Limited to one indel or splice site per read Does not use read quality scores FU Berlin, GSNAP, Omics WS
112 Limitations Matching is only exhaustive if there is at least a 14nt contiguous match, otherwise it s a heuristic Limited to one indel or splice site per read Does not use read quality scores Does not work with ABI SOLiD data FU Berlin, GSNAP, Omics WS
113 TL;DR Comparable to other fast read alignment algorithms in terms of speed, but can handle more complex variants and splicing FU Berlin, GSNAP, Omics WS
114 For Further Reading I Knuth D.E. The Art of Computer Programming: Sorting and Searching. Vol 3. Addison-Wesley, 1973 Thomas D. Wu and Serban Nacu Fast and SNP-tolerant detection of complex variants and splicing in short reads Bioinformatics, 2010 Apr 1;26(7): Yeo G and Burge CB. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals J Comput Biol. 2004;11(2-3): FU Berlin, GSNAP, Omics WS
115 Aligning Paired-End Reads Optional FU Berlin, GSNAP, Omics WS
116 Bisulfite-converted DNA Optional FU Berlin, GSNAP, Omics WS
Under the Hood of Alignment Algorithms for NGS Researchers
Under the Hood of Alignment Algorithms for NGS Researchers April 16, 2014 Gabe Rudy VP of Product Development Golden Helix Questions during the presentation Use the Questions pane in your GoToWebinar window
Scalable RNA Sequencing on Clusters of Multicore Processors
JOAQUÍN DOPAZO JOAQUÍN TARRAGA SERGIO BARRACHINA MARÍA ISABEL CASTILLO HÉCTOR MARTÍNEZ ENRIQUE S. QUINTANA ORTÍ IGNACIO MEDINA INTRODUCTION DNA Exon 0 Exon 1 Exon 2 Intron 0 Intron 1 Reads Sequencing RNA
Sequencing. Short Read Alignment. Sequencing. Paired-End Sequencing 6/10/2010. Tobias Rausch 7 th June 2010 WGS. ChIP-Seq. Applied Biosystems.
Sequencing Short Alignment Tobias Rausch 7 th June 2010 WGS RNA-Seq Exon Capture ChIP-Seq Sequencing Paired-End Sequencing Target genome Fragments Roche GS FLX Titanium Illumina Applied Biosystems SOLiD
Long Read RNA-seq Mapper
UNIVERSITY OF ZAGREB FACULTY OF ELECTRICAL ENGENEERING AND COMPUTING MASTER THESIS no. 1005 Long Read RNA-seq Mapper Josip Marić Zagreb, February 2015. Table of Contents 1. Introduction... 1 2. RNA Sequencing...
Read Mapping. Slides by Carl Kingsford
Read Mapping Slides by Carl Kingsford Bowtie Ultrafast and memory-efficient alignment of short DNA sequences to the human genome Ben Langmead, Cole Trapnell, Mihai Pop and Steven L Salzberg, Genome Biology
High-throughput sequencing: Alignment and related topic. Simon Anders EMBL Heidelberg
High-throughput sequencing: Alignment and related topic Simon Anders EMBL Heidelberg Established platforms HTS Platforms Illumina HiSeq, ABI SOLiD, Roche 454 Newcomers: Benchtop machines 454 GS Junior,
RNA-seq. Manpreet S. Katari
RNA-seq Manpreet S. Katari Evolution of Sequence Technology Normalizing the Data RPKM (Reads per Kilobase of exons per million reads) Score = R NT R = # of unique reads for the gene N = Size of the gene
Rsubread package: high-performance read alignment, quantification and mutation discovery
Rsubread package: high-performance read alignment, quantification and mutation discovery Wei Shi 14 September 2015 1 Introduction This vignette provides a brief description to the Rsubread package. For
Rsubread package: high-performance read alignment, quantification and mutation discovery
Rsubread package: high-performance read alignment, quantification and mutation discovery Wei Shi 14 September 2015 1 Introduction This vignette provides a brief description to the Rsubread package. For
High-throughput sequencing: Alignment and related topic. Simon Anders EMBL Heidelberg
High-throughput sequencing: Alignment and related topic Simon Anders EMBL Heidelberg Established platforms HTS Platforms Illumina HiSeq, ABI SOLiD, Roche 454 Newcomers: Benchtop machines: Illumina MiSeq,
Review of Recent NGS Short Reads Alignment Tools BMI-231 final project, Chenxi Chen Spring 2014
Review of Recent NGS Short Reads Alignment Tools BMI-231 final project, Chenxi Chen Spring 2014 Deciphering the information contained in DNA sequences began decades ago since the time of Sanger sequencing.
Welcome to MAPHiTS (Mapping Analysis Pipeline for High-Throughput Sequences) tutorial page.
Welcome to MAPHiTS (Mapping Analysis Pipeline for High-Throughput Sequences) tutorial page. In this page you will learn to use the tools of the MAPHiTS suite. A little advice before starting : rename your
Short Read Alignment. Mapping Reads to a Reference
Short Read Alignment Mapping Reads to a Reference Brandi Cantarel, Ph.D. & Daehwan Kim, Ph.D. BICF 05/2018 Introduction to Mapping Short Read Aligners DNA vs RNA Alignment Quality Pitfalls and Improvements
High-throughout sequencing and using short-read aligners. Simon Anders
High-throughout sequencing and using short-read aligners Simon Anders High-throughput sequencing (HTS) Sequencing millions of short DNA fragments in parallel. a.k.a.: next-generation sequencing (NGS) massively-parallel
As of August 15, 2008, GenBank contained bases from reported sequences. The search procedure should be
48 Bioinformatics I, WS 09-10, S. Henz (script by D. Huson) November 26, 2009 4 BLAST and BLAT Outline of the chapter: 1. Heuristics for the pairwise local alignment of two sequences 2. BLAST: search and
Short Read Alignment Algorithms
Short Read Alignment Algorithms Raluca Gordân Department of Biostatistics and Bioinformatics Department of Computer Science Department of Molecular Genetics and Microbiology Center for Genomic and Computational
KisSplice. Identifying and Quantifying SNPs, indels and Alternative Splicing Events from RNA-seq data. 29th may 2013
Identifying and Quantifying SNPs, indels and Alternative Splicing Events from RNA-seq data 29th may 2013 Next Generation Sequencing A sequencing experiment now produces millions of short reads ( 100 nt)
NGS Analysis Using Galaxy
NGS Analysis Using Galaxy Sequences and Alignment Format Galaxy overview and Interface Get;ng Data in Galaxy Analyzing Data in Galaxy Quality Control Mapping Data History and workflow Galaxy Exercises
BIOINFORMATICS APPLICATIONS NOTE
BIOINFORMATICS APPLICATIONS NOTE Sequence analysis BRAT: Bisulfite-treated Reads Analysis Tool (Supplementary Methods) Elena Y. Harris 1,*, Nadia Ponts 2, Aleksandr Levchuk 3, Karine Le Roch 2 and Stefano
Bioinformatics in next generation sequencing projects
Bioinformatics in next generation sequencing projects Rickard Sandberg Assistant Professor Department of Cell and Molecular Biology Karolinska Institutet March 2011 Once sequenced the problem becomes computational
all M 2M_gt_15 2M_8_15 2M_1_7 gt_2m TopHat2
Pairs processed per second 6, 4, 2, 6, 4, 2, 6, 4, 2, 6, 4, 2, 6, 4, 2, 6, 4, 2, 72,318 418 1,666 49,495 21,123 69,984 35,694 1,9 71,538 3,5 17,381 61,223 69,39 55 19,579 44,79 65,126 96 5,115 33,6 61,787
Identiyfing splice junctions from RNA-Seq data
Identiyfing splice junctions from RNA-Seq data Joseph K. Pickrell pickrell@uchicago.edu October 4, 2010 Contents 1 Motivation 2 2 Identification of potential junction-spanning reads 2 3 Calling splice
When we search a nucleic acid databases, there is no need for you to carry out your own six frame translation. Mascot always performs a 6 frame
1 When we search a nucleic acid databases, there is no need for you to carry out your own six frame translation. Mascot always performs a 6 frame translation on the fly. That is, 3 reading frames from
Read Mapping and Assembly
Statistical Bioinformatics: Read Mapping and Assembly Stefan Seemann seemann@rth.dk University of Copenhagen April 9th 2019 Why sequencing? Why sequencing? Which organism does the sample comes from? Assembling
Wilson Leung 05/27/2008 A Simple Introduction to NCBI BLAST
A Simple Introduction to NCBI BLAST Prerequisites: Detecting and Interpreting Genetic Homology: Lecture Notes on Alignment Resources: The BLAST web server is available at http://www.ncbi.nih.gov/blast/
On enhancing variation detection through pan-genome indexing
Standard approach...t......t......t......acgatgctagtgcatgt......t......t......t... reference genome Variation graph reference SNP: A->T...ACGATGCTTGTGCATGT donor genome Can we boost variation detection
v0.2.0 XX:Z:UA - Unassigned XX:Z:G1 - Genome 1-specific XX:Z:G2 - Genome 2-specific XX:Z:CF - Conflicting
October 08, 2015 v0.2.0 SNPsplit is an allele-specific alignment sorter which is designed to read alignment files in SAM/ BAM format and determine the allelic origin of reads that cover known SNP positions.
Omega: an Overlap-graph de novo Assembler for Metagenomics
Omega: an Overlap-graph de novo Assembler for Metagenomics B a h l e l H a i d e r, Ta e - H y u k A h n, B r i a n B u s h n e l l, J u a n j u a n C h a i, A l e x C o p e l a n d, C h o n g l e Pa n
Sequence Analysis Pipeline
Sequence Analysis Pipeline Transcript fragments 1. PREPROCESSING 2. ASSEMBLY (today) Removal of contaminants, vector, adaptors, etc Put overlapping sequence together and calculate bigger sequences 3. Analysis/Annotation
Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs
Masher: Mapping Long(er) Reads with Hash-based Genome Indexing on GPUs Anas Abu-Doleh 1,2, Erik Saule 1, Kamer Kaya 1 and Ümit V. Çatalyürek 1,2 1 Department of Biomedical Informatics 2 Department of Electrical
Sequence mapping and assembly. Alistair Ward - Boston College
Sequence mapping and assembly Alistair Ward - Boston College Sequenced a genome? Fragmented a genome -> DNA library PCR amplification Sequence reads (ends of DNA fragment for mate pairs) We no longer have
NA12878 Platinum Genome GENALICE MAP Analysis Report
NA12878 Platinum Genome GENALICE MAP Analysis Report Bas Tolhuis, PhD Jan-Jaap Wesselink, PhD GENALICE B.V. INDEX EXECUTIVE SUMMARY...4 1. MATERIALS & METHODS...5 1.1 SEQUENCE DATA...5 1.2 WORKFLOWS......5
REPORT. NA12878 Platinum Genome. GENALICE MAP Analysis Report. Bas Tolhuis, PhD GENALICE B.V.
REPORT NA12878 Platinum Genome GENALICE MAP Analysis Report Bas Tolhuis, PhD GENALICE B.V. INDEX EXECUTIVE SUMMARY...4 1. MATERIALS & METHODS...5 1.1 SEQUENCE DATA...5 1.2 WORKFLOWS......5 1.3 ACCURACY
arxiv: v2 [q-bio.gn] 13 May 2014
![arxiv: v2 [q-bio.gn] 13 May 2014](/thumbs/81/83096958.jpg "arxiv: v2 [q-bio.gn] 13 May 2014")
BIOINFORMATICS Vol. 00 no. 00 2005 Pages 1 2 Fast and accurate alignment of long bisulfite-seq reads Brent S. Pedersen 1,, Kenneth Eyring 1, Subhajyoti De 1,2, Ivana V. Yang 1 and David A. Schwartz 1 1
Aligners. J Fass 21 June 2017
Aligners J Fass 21 June 2017 Definitions Assembly: I ve found the shredded remains of an important document; put it back together! UC Davis Genome Center Bioinformatics Core J Fass Aligners 2017-06-21
From Smith-Waterman to BLAST
From Smith-Waterman to BLAST Jeremy Buhler July 23, 2015 Smith-Waterman is the fundamental tool that we use to decide how similar two sequences are. Isn t that all that BLAST does? In principle, it is
Genome 373: Mapping Short Sequence Reads I. Doug Fowler
Genome 373: Mapping Short Sequence Reads I Doug Fowler Two different strategies for parallel amplification BRIDGE PCR EMULSION PCR Two different strategies for parallel amplification BRIDGE PCR EMULSION
Indexing. UCSB 290N. Mainly based on slides from the text books of Croft/Metzler/Strohman and Manning/Raghavan/Schutze
Indexing UCSB 290N. Mainly based on slides from the text books of Croft/Metzler/Strohman and Manning/Raghavan/Schutze All slides Addison Wesley, 2008 Table of Content Inverted index with positional information
GenomeStudio Software Release Notes
GenomeStudio Software 2009.2 Release Notes 1. GenomeStudio Software 2009.2 Framework... 1 2. Illumina Genome Viewer v1.5...2 3. Genotyping Module v1.5... 4 4. Gene Expression Module v1.5... 6 5. Methylation
From the Schnable Lab:
From the Schnable Lab: Yang Zhang and Daniel Ngu s Pipeline for Processing RNA-seq Data (As of November 17, 2016) yzhang91@unl.edu dngu2@huskers.unl.edu Pre-processing the reads: The alignment software
Mapping NGS reads for genomics studies
Mapping NGS reads for genomics studies Valencia, 28-30 Sep 2015 BIER Alejandro Alemán aaleman@cipf.es Genomics Data Analysis CIBERER Where are we? Fastq Sequence preprocessing Fastq Alignment BAM Visualization
SMALT Manual. December 9, 2010 Version 0.4.2
SMALT Manual December 9, 2010 Version 0.4.2 Abstract SMALT is a pairwise sequence alignment program for the efficient mapping of DNA sequencing reads onto genomic reference sequences. It uses a combination
Ensembl RNASeq Practical. Overview
Ensembl RNASeq Practical The aim of this practical session is to use BWA to align 2 lanes of Zebrafish paired end Illumina RNASeq reads to chromosome 12 of the zebrafish ZV9 assembly. We have restricted
Wilson Leung 01/03/2018 An Introduction to NCBI BLAST. Prerequisites: Detecting and Interpreting Genetic Homology: Lecture Notes on Alignment
An Introduction to NCBI BLAST Prerequisites: Detecting and Interpreting Genetic Homology: Lecture Notes on Alignment Resources: The BLAST web server is available at https://blast.ncbi.nlm.nih.gov/blast.cgi
SlopMap: a software application tool for quick and flexible identification of similar sequences using exact k-mer matching
SlopMap: a software application tool for quick and flexible identification of similar sequences using exact k-mer matching Ilya Y. Zhbannikov 1, Samuel S. Hunter 1,2, Matthew L. Settles 1,2, and James
Illumina Next Generation Sequencing Data analysis
Illumina Next Generation Sequencing Data analysis Chiara Dal Fiume Sr Field Application Scientist Italy 2010 Illumina, Inc. All rights reserved. Illumina, illuminadx, Solexa, Making Sense Out of Life,
Omixon PreciseAlign CLC Genomics Workbench plug-in
Omixon PreciseAlign CLC Genomics Workbench plug-in User Manual User manual for Omixon PreciseAlign plug-in CLC Genomics Workbench plug-in (all platforms) CLC Genomics Server plug-in (all platforms) January
RNA-seq Data Analysis
Seyed Abolfazl Motahari RNA-seq Data Analysis Basics Next Generation Sequencing Biological Samples Data Cost Data Volume Big Data Analysis in Biology تحلیل داده ها کنترل سیستمهای بیولوژیکی تشخیص بیماریها
Inverted Indexes. Indexing and Searching, Modern Information Retrieval, Addison Wesley, 2010 p. 5
Inverted Indexes Indexing and Searching, Modern Information Retrieval, Addison Wesley, 2010 p. 5 Basic Concepts Inverted index: a word-oriented mechanism for indexing a text collection to speed up the
Data Preprocessing. Next Generation Sequencing analysis DTU Bioinformatics Next Generation Sequencing Analysis
Data Preprocessing Next Generation Sequencing analysis DTU Bioinformatics Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads
Mapping, Alignment and SNP Calling
Mapping, Alignment and SNP Calling Heng Li Broad Institute MPG Next Gen Workshop 2011 Heng Li (Broad Institute) Mapping, alignment and SNP calling 17 February 2011 1 / 19 Outline 1 Mapping Messages from
GPUBwa -Parallelization of Burrows Wheeler Aligner using Graphical Processing Units
GPUBwa -Parallelization of Burrows Wheeler Aligner using Graphical Processing Units Abstract A very popular discipline in bioinformatics is Next-Generation Sequencing (NGS) or DNA sequencing. It specifies
INTRODUCTION AUX FORMATS DE FICHIERS
INTRODUCTION AUX FORMATS DE FICHIERS Plan. Formats de séquences brutes.. Format fasta.2. Format fastq 2. Formats d alignements 2.. Format SAM 2.2. Format BAM 4. Format «Variant Calling» 4.. Format Varscan
Supplementary Figure 1. Fast read-mapping algorithm of BrowserGenome.
Supplementary Figure 1 Fast read-mapping algorithm of BrowserGenome. (a) Indexing strategy: The genome sequence of interest is divided into non-overlapping 12-mers. A Hook table is generated that contains
Data Preprocessing : Next Generation Sequencing analysis CBS - DTU Next Generation Sequencing Analysis
Data Preprocessing 27626: Next Generation Sequencing analysis CBS - DTU Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads
Analysis of ChIP-seq data
Before we start: 1. Log into tak (step 0 on the exercises) 2. Go to your lab space and create a folder for the class (see separate hand out) 3. Connect to your lab space through the wihtdata network and
Exon Probeset Annotations and Transcript Cluster Groupings
Exon Probeset Annotations and Transcript Cluster Groupings I. Introduction This whitepaper covers the procedure used to group and annotate probesets. Appropriate grouping of probesets into transcript clusters
NextGenMap and the impact of hhighly polymorphic regions. Arndt von Haeseler
NextGenMap and the impact of hhighly polymorphic regions Arndt von Haeseler Joint work with: The Technological Revolution Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program
Advanced UCSC Browser Functions
Advanced UCSC Browser Functions Dr. Thomas Randall tarandal@email.unc.edu bioinformatics.unc.edu UCSC Browser: genome.ucsc.edu Overview Custom Tracks adding your own datasets Utilities custom tools for
Galaxy Platform For NGS Data Analyses
Galaxy Platform For NGS Data Analyses Weihong Yan wyan@chem.ucla.edu Collaboratory Web Site http://qcb.ucla.edu/collaboratory Collaboratory Workshops Workshop Outline ü Day 1 UCLA galaxy and user account
Read mapping with BWA and BOWTIE
Read mapping with BWA and BOWTIE Before We Start In order to save a lot of typing, and to allow us some flexibility in designing these courses, we will establish a UNIX shell variable BASE to point to
Next Generation Sequence Alignment on the BRC Cluster. Steve Newhouse 22 July 2010
Next Generation Sequence Alignment on the BRC Cluster Steve Newhouse 22 July 2010 Overview Practical guide to processing next generation sequencing data on the cluster No details on the inner workings
The software comes with 2 installers: (1) SureCall installer (2) GenAligners (contains BWA, BWA- MEM).
Release Notes Agilent SureCall 4.0 Product Number G4980AA SureCall Client 6-month named license supports installation of one client and server (to host the SureCall database) on one machine. For additional
Package Rsubread. July 21, 2013
Package Rsubread July 21, 2013 Type Package Title Rsubread: an R package for the alignment, summarization and analyses of next-generation sequencing data Version 1.10.5 Author Wei Shi and Yang Liao with
Genetics 211 Genomics Winter 2014 Problem Set 4
Genomics - Part 1 due Friday, 2/21/2014 by 9:00am Part 2 due Friday, 3/7/2014 by 9:00am For this problem set, we re going to use real data from a high-throughput sequencing project to look for differential
SEASHORE / SARUMAN. Short Read Matching using GPU Programming. Tobias Jakobi
SEASHORE SARUMAN Summary 1 / 24 SEASHORE / SARUMAN Short Read Matching using GPU Programming Tobias Jakobi Center for Biotechnology (CeBiTec) Bioinformatics Resource Facility (BRF) Bielefeld University
Computational Molecular Biology
Computational Molecular Biology Erwin M. Bakker Lecture 3, mainly from material by R. Shamir [2] and H.J. Hoogeboom [4]. 1 Pairwise Sequence Alignment Biological Motivation Algorithmic Aspect Recursive
Darwin-WGA. A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup
Darwin-WGA A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup Yatish Turakhia*, Sneha D. Goenka*, Prof. Gill Bejerano, Prof. William J. Dally * Equal contribution
ASAP - Allele-specific alignment pipeline
ASAP - Allele-specific alignment pipeline Jan 09, 2012 (1) ASAP - Quick Reference ASAP needs a working version of Perl and is run from the command line. Furthermore, Bowtie needs to be installed on your
Introduction to Read Alignment. UCD Genome Center Bioinformatics Core Tuesday 15 September 2015
Introduction to Read Alignment UCD Genome Center Bioinformatics Core Tuesday 15 September 2015 From reads to molecules Why align? Individual A Individual B ATGATAGCATCGTCGGGTGTCTGCTCAATAATAGTGCCGTATCATGCTGGTGTTATAATCGCCGCATGACATGATCAATGG
USING BRAT-BW Table 1. Feature comparison of BRAT-bw, BRAT-large, Bismark and BS Seeker (as of on March, 2012)
USING BRAT-BW-2.0.1 BRAT-bw is a tool for BS-seq reads mapping, i.e. mapping of bisulfite-treated sequenced reads. BRAT-bw is a part of BRAT s suit. Therefore, input and output formats for BRAT-bw are
Preliminary Syllabus. Genomics. Introduction & Genome Assembly Sequence Comparison Gene Modeling Gene Function Identification
Preliminary Syllabus Sep 30 Oct 2 Oct 7 Oct 9 Oct 14 Oct 16 Oct 21 Oct 25 Oct 28 Nov 4 Nov 8 Introduction & Genome Assembly Sequence Comparison Gene Modeling Gene Function Identification OCTOBER BREAK
v0.3.0 May 18, 2016 SNPsplit operates in two stages:
May 18, 2016 v0.3.0 SNPsplit is an allele-specific alignment sorter which is designed to read alignment files in SAM/ BAM format and determine the allelic origin of reads that cover known SNP positions.
High-performance short sequence alignment with GPU acceleration
Distrib Parallel Databases (2012) 30:385 399 DOI 10.1007/s10619-012-7099-x High-performance short sequence alignment with GPU acceleration Mian Lu Yuwei Tan Ge Bai Qiong Luo Published online: 10 August
Benchmarking of RNA-seq aligners
Lecture 17 RNA-seq Alignment STAR Benchmarking of RNA-seq aligners Benchmarking of RNA-seq aligners Benchmarking of RNA-seq aligners Benchmarking of RNA-seq aligners Based on this analysis the most reliable
Indexing and Searching
Indexing and Searching Introduction How to retrieval information? A simple alternative is to search the whole text sequentially Another option is to build data structures over the text (called indices)
Comparison between conventional read mapping methods and compressively-accelerated read mapping framework (CORA).
Supplementary Figure 1 Comparison between conventional read mapping methods and compressively-accelerated read mapping framework (CORA). Comparison between conventional read mapping methods and compressively-accelerated
NGS Data and Sequence Alignment
Applications and Servers SERVER/REMOTE Compute DB WEB Data files NGS Data and Sequence Alignment SSH WEB SCP Manpreet S. Katari App Aug 11, 2016 Service Terminal IGV Data files Window Personal Computer/Local
Lecture Overview. Sequence search & alignment. Searching sequence databases. Sequence Alignment & Search. Goals: Motivations:
Lecture Overview Sequence Alignment & Search Karin Verspoor, Ph.D. Faculty, Computational Bioscience Program University of Colorado School of Medicine With credit and thanks to Larry Hunter for creating
de novo assembly Simon Rasmussen 36626: Next Generation Sequencing analysis DTU Bioinformatics Next Generation Sequencing Analysis
de novo assembly Simon Rasmussen 36626: Next Generation Sequencing analysis DTU Bioinformatics 27626 - Next Generation Sequencing Analysis Generalized NGS analysis Data size Application Assembly: Compare
Supplementary Information. Detecting and annotating genetic variations using the HugeSeq pipeline
Supplementary Information Detecting and annotating genetic variations using the HugeSeq pipeline Hugo Y. K. Lam 1,#, Cuiping Pan 1, Michael J. Clark 1, Phil Lacroute 1, Rui Chen 1, Rajini Haraksingh 1,
Sequence Alignment & Search
Sequence Alignment & Search Karin Verspoor, Ph.D. Faculty, Computational Bioscience Program University of Colorado School of Medicine With credit and thanks to Larry Hunter for creating the first version
RNA-seq. Read mapping and Quantification. Genomics: Lecture #12. Institut für Medizinische Genetik und Humangenetik Charité Universitätsmedizin Berlin
(1) Read and Quantification Institut für Medizinische Genetik und Humangenetik Charité Universitätsmedizin Berlin Genomics: Lecture #12 Today (1) Gene Expression Previous gold standard: Basic protocol
splitmem: graphical pan-genome analysis with suffix skips Shoshana Marcus May 7, 2014
splitmem: graphical pan-genome analysis with suffix skips Shoshana Marcus May 7, 2014 Outline 1 Overview 2 Data Structures 3 splitmem Algorithm 4 Pan-genome Analysis Objective Input! Output! A B C D Several
CLC Server. End User USER MANUAL
CLC Server End User USER MANUAL Manual for CLC Server 10.0.1 Windows, macos and Linux March 8, 2018 This software is for research purposes only. QIAGEN Aarhus Silkeborgvej 2 Prismet DK-8000 Aarhus C Denmark
NGS Data Analysis. Roberto Preste
NGS Data Analysis Roberto Preste 1 Useful info http://bit.ly/2r1y2dr Contacts: roberto.preste@gmail.com Slides: http://bit.ly/ngs-data 2 NGS data analysis Overview 3 NGS Data Analysis: the basic idea http://bit.ly/2r1y2dr
Intro to NGS Tutorial
Intro to NGS Tutorial Release 8.6.0 Golden Helix, Inc. October 31, 2016 Contents 1. Overview 2 2. Import Variants and Quality Fields 3 3. Quality Filters 10 Generate Alternate Read Ratio.........................................
Building approximate overlap graphs for DNA assembly using random-permutations-based search.
An algorithm is presented for fast construction of graphs of reads, where an edge between two reads indicates an approximate overlap between the reads. Since the algorithm finds approximate overlaps directly,
Tutorial 1: Exploring the UCSC Genome Browser
Last updated: May 12, 2011 Tutorial 1: Exploring the UCSC Genome Browser Open the homepage of the UCSC Genome Browser at: http://genome.ucsc.edu/ In the blue bar at the top, click on the Genomes link.
Sequence Alignment (chapter 6) p The biological problem p Global alignment p Local alignment p Multiple alignment
Sequence lignment (chapter 6) p The biological problem p lobal alignment p Local alignment p Multiple alignment Local alignment: rationale p Otherwise dissimilar proteins may have local regions of similarity
BGGN-213: FOUNDATIONS OF BIOINFORMATICS (Lecture 14)
BGGN-213: FOUNDATIONS OF BIOINFORMATICS (Lecture 14) Genome Informatics (Part 1) https://bioboot.github.io/bggn213_f17/lectures/#14 Dr. Barry Grant Nov 2017 Overview: The purpose of this lab session is
Aligning reads: tools and theory
Aligning reads: tools and theory Genome Sequence read :LM-Mel-14neg :LM-Mel-42neg :LM-Mel-14neg :LM-Mel-14pos :LM-Mel-42neg :LM-Mel-14neg :LM-Mel-42neg :LM-Mel-14neg chrx: 152139280 152139290 152139300
BRAT-BW: Efficient and accurate mapping of bisulfite-treated reads [Supplemental Material]
![BRAT-BW: Efficient and accurate mapping of bisulfite-treated reads [Supplemental Material]](/thumbs/72/66393769.jpg "BRAT-BW: Efficient and accurate mapping of bisulfite-treated reads [Supplemental Material]")
BRAT-BW: Efficient and accurate mapping of bisulfite-treated reads [Supplemental Material] Elena Y. Harris 1, Nadia Ponts 2,3, Karine G. Le Roch 2 and Stefano Lonardi 1 1 Department of Computer Science
A Fast Read Alignment Method based on Seed-and-Vote For Next GenerationSequencing
A Fast Read Alignment Method based on Seed-and-Vote For Next GenerationSequencing Song Liu 1,2, Yi Wang 3, Fei Wang 1,2 * 1 Shanghai Key Lab of Intelligent Information Processing, Shanghai, China. 2 School
m6aviewer Version Documentation
m6aviewer Version 1.6.0 Documentation Contents 1. About 2. Requirements 3. Launching m6aviewer 4. Running Time Estimates 5. Basic Peak Calling 6. Running Modes 7. Multiple Samples/Sample Replicates 8.
Running SNAP. The SNAP Team October 2012
Running SNAP The SNAP Team October 2012 1 Introduction SNAP is a tool that is intended to serve as the read aligner in a gene sequencing pipeline. Its theory of operation is described in Faster and More
CloudBurst: Highly Sensitive Read Mapping with MapReduce
Bioinformatics Advance Access published April 8, 2009 Sequence Analysis CloudBurst: Highly Sensitive Read Mapping with MapReduce Michael C. Schatz* Center for Bioinformatics and Computational Biology,
Lecture 12. Short read aligners
Lecture 12 Short read aligners Ebola reference genome We will align ebola sequencing data against the 1976 Mayinga reference genome. We will hold the reference gnome and all indices: mkdir -p ~/reference/ebola
Dindel User Guide, version 1.0
Dindel User Guide, version 1.0 Kees Albers University of Cambridge, Wellcome Trust Sanger Institute caa@sanger.ac.uk October 26, 2010 Contents 1 Introduction 2 2 Requirements 2 3 Optional input 3 4 Dindel
Indexing and Searching
Indexing and Searching Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University References: 1. Modern Information Retrieval, chapter 9 2. Information Retrieval: