RNA-seq Data Analysis

Transcription

1 Seyed Abolfazl Motahari RNA-seq Data Analysis Basics

2 Next Generation Sequencing Biological Samples Data Cost Data Volume

3 Big Data Analysis in Biology تحلیل داده ها کنترل سیستمهای بیولوژیکی تشخیص بیماریها تولید مواد بیولوژیکی جدید محافظت بیولوژیکی طراحی داروها

4 Read The sequence generated by a sequencing machine from a DNA fragment. DNA Fragment Sequencer Read ACCGTAACCTACTTAGTA

5 Paired-end Read Data from a pair of reads sequenced from ends of the same DNA fragment. The genomic distance between the reads is approximately known and is used to constrain assembly solutions. DNA Fragment Sequencer Reads ACCGTAACCTACTTAGTA CTAGTAACCTTAACCGTA

6 Mate-pair Read Long DNA Fragment 12kb Biotinylated B B B B B B B B Shearing Capture Similar to the paired-end reads with longer inserts

7 Sequencing Technologies Basic Methods Maxam-Gilbert Sequencing Sanger Sequencing Next Generation Methods Polony Sequencing 454 Pyrosequencing Illumina Sequencing SOLiD Sequencing Ion Torrent Semiconductor Sequencing DNA Nanoball Sequncing Heliscope Signle Molecule Sequencing Signle Molecule Real-time Sequencing Methods in Development Nanopore DNA Sequencing Tunneling Currents DNA Sequencing Sequencing by Hybridization Sequencing with Mass Spectrometry Microfluidic Sanger Sequencing Microscopy-based Sequencing

8 Comparison of Sequencers Technology Read Length Error Rate Paired-end Sanger up to 2k 2% Yes ABI/SOLiD 75 2% Yes Illumina % Yes Roche/ % No IonTorrent 200 4% No PacBio up to 15k 18% Yes Potentially all sequencing technologies can be used to sequence mate-pair libraries obtained by the circularization of long DNA fragments

9 NGS Applications Biological samples Genomes (DNA) Transcriptome (RNA) Whole Genome Size selection Capturing Regions mirna-seq RNA-seq Whole Exome Epigenomics Capturing ChIP-seq methyl-seq DNase-seq CLIP-seq

10 Typical Workflow DNA Prep Raw Data Library Prep Sequencing Chip Prep Preprocessing App Assembly De Novo/Alignment

11 Tasks

12 UNIX Advantages of Unix - Unix is more flexible and can be installed on many different types of machines, including main-frame computers, supercomputers and micro-computers. - Unix is more stable and does not go down as often as Windows does, therefore requires less administration and maintenance. - Unix has greater built-in security and permissions features than Windows. - Unix possesses much greater processing power than Windows. - Unix is the leader in serving the Web. About 90% of the Internet relies on Unix operating systems running on Apache, the worlds most widely used Web server, which is free. - Software upgrades from Microsoft often require the user to purchase new or more hardware or prerequisite software. That is not the case with Unix. - The mostly free or inexpensive open-source operating systems, such as Linux and BSD, with their flexibility and control, prove to be very attractive to (aspiring) computer wizards. Many of the smartest programmers are developing state-of-the-art software free of charge for the fast growing "open-source movement. - Unix also inspires novel approaches to software design, such as solving problems by interconnecting simpler tools instead of creating large monolithic application programs.

13 UNIX

14 Command Line Command Line Interface (CLI) Graphical User Interface (GUI)

15 General Command command [-options] [args...] The prompt The current directory ( path ) The host MacBook-Pro:abolfazl$ bowtie -v 3 -S human reads.fastq > aligned.sam

16 Unix File System / Applications bin home use Abolfazl Sajjad Data /home/abolfazl/data The Path

17 Genome Format Fasta Format Header Sequence >gi ref NC_ Yersinia enterocolitica subsp. enterocolitica 8081 chromosome, complete genome GATCTTTTTATTTAAAGATCTCTTTATTAGATCTCTTATTAGGATCATGACCTTCTGTGGATAAGTGATT ATTCACATTTAAGATCATGTGATTAAGGAGGATCGTTTGCTGTGAATGATCGGTGATCCTATTGCGTATA AGCTGGGATCTAAATGGCATGTTATGCACAGGCACTTTAAGTTACTAAGGTTGTTATGTGGATATGTACT GCTTATACCCTGCTTTCAAGCTTACTTATCCACATTCGTTCGCGTGATCTTTAAGCAAATTAGAGTAAAT TAATCCAGTTTTTAACCCAAATCTCTGCCGGATCCTCAGGAATTTCATGTTTGATGACGTCAATTTCTAA AATATCACCCACACGAATGGCTCCCTGGATGATCAGTTGCTGATCCAATTTTCTGACCGCACCACAGAAA GTGTCATATTCTGAACTGCCCAAACCAACAGCACCAAAGCGAACCTGTGAGAGATCCGGTCTCTGCTGCT CGATTTGTTCTAATAAGGGTTGAAGATTGTCTGGCAGATCACCTGCACCATGAGTGGAAGTGATTATTAG CCACATACCATCCAAGGTCAGCTCGTCTAATTCCGGGCCATGCAGAGTTTCTGTCGTGAAACCCGCCTCT TCTAATTTCTCAGCTAAATGTTCAGCAACGTATTCAGCACTGCCAAGCGTACTGCCACTGATCAAGGTAA TGTCAGCCATAAAGACCCCAACCGAAGTAATGAACCGGTATTGTACGCTGTGAATCAGCTGGGATCTACC TGTGGATAATGTGGGTATAGTTATTTAGTGCTCAGGGCACGATGGTACGCATGATGGGGTTTTGCAGGGA AATAAGAGTCTCGGTTGACTGGATCTCATCAATAGTTTGGATCTTGTTGATAAGTACCTGTTGCAGTGCA TCTATCGATTTACACATGACCTTAATAAAGATGCTGTAATGGCCAGTGGTGTAATAGGCCTCGACAACTT CTTCTAAACTTTCCAGTTTTTTTAATGCAGAAGGGTAATCTTTGGCACTTTTCAAAATGATGCCGATGAA

18 Read Format (Fastq) FASTQ files extend FASTA files in that they provide both sequence and quality. A FASTQ file thus typically consists of four lines. 1. A line starting containing the sequence identifier 2. the actual sequence 3. a line starting with + after which the sequence identifier is optional 4. a line with quality values which are encoded in ASCII space As such the 2nd and 4th line must have the same length One such entry is given below showing one sequence "ATGTCT".. Header 1:N:0: ATGTCTCCTGGACCCCTCTGTGCCCAAGCTCCTCATGCATCCTCCTCAGCAACTTGTCCTGTAGCTGAGGCTCACTGACTACCAGCTGCAG + 1:DAADDDF<B<AGF=FGIEHCCD9DG=1E9?D>CF@HHG??B<GEBGHCG;;CDB8==C@@>>GII@@5?A?@B>CEDCFCC:;?CCCAC Quality Scores

19 Figure 1 Strategies for reconstructing transcripts from RNA-Seq reads. The align-then-assemble 4 5 RNA-seq NEWS AND VIEWS WHY? 1- To assemble the transcriptome 2- To find the expression levels Assembly Paradigms: (depending on other sources) 1- De Novo 2- Genome Guided 3- Transcriptome Guided RNA-Seq reads Align reads to genome Assemble transcripts de novo Genome Assemble transcripts from spliced alignments Align transcripts to genome More abundant Less abundant

20 De Novo Assembly Transcriptome Assembly Reads Quantitative Analysis

21 Assembly with Available Genome DNA RNA Genome Transcriptome Assembly RNA- seq Reads Quantitative Analysis

22 Assembly with Available Transcriptome RNA RNA Transcriptome RNA- seq Reads Quantitative Analysis

23 Assembly with Available Genome

24 Pipeline Sequencing Quality Control Read Alignment Transcript assembly Gene identification RNA- seq reads (2 x 100 bp) FASTX/FastQC Bowtie/Tophat Cufflinks Cufflinks (cuffmerge) RNA- seq reads (2 x 100 bp) (.fastq files) Reference genome (.fasta files) Gene annotation (.gtf files) Visualization Differential expression IGV Cuffdiff (A:B Comparison)

25 Quality Control Quality assessment is the first step of the bioinformatics pipeline of RNA-Seq. Filtering data removing low quality sequences or bases (trimming), adaptors, contaminations or overrepresented sequences to assure a coherent final result. Packages: FastQC developed in Java. Results are presented in HTML permanent reports. FASTX conversion from FASTQ to FASTA format information about statistics of quality removing sequencing adapters, filtering and cutting sequences based on quality

26 FastQC

27 FASTX

28 Read Mapping Strategies unspliced aligner bowtie bwa soap spliced aligner Short Read Aligners tophat MapSplice SpliceMap Garber et al. Nature Methods 8, (2011)

29 Aligners RNA Bisulfite DNA microrna

30 Short Read Aligners Name Description Use FAST Q Gap ped pairedend Multithreaded Bowtie Based on Burrows-Wheeler transform 1.3 GB memory footprint for human genome. Aligns more than 25 million Illumina reads in 1 CPU hour. Yes Yes No Yes BWA Based on Burrows-Wheeler transform It's a bit slower than bowtie but allows indels in alignment. Yes No Yes Yes SHRiMP Indexes the reference genome as of version 2. Uses masks to generate possible keys. Yes Yes Yes Yes SOAP, SOAP2, SOAP3 and SOAP3-dp SOAP: Robust with a small (1-3) number of gaps and mismatches. SOAP2: using bidirectional BWT much faster than the first version. SOAP3: GPU- accelerated version SOAP3- dp, also GPU accelerated, Yes No SOA P3- dp:y es Yes

31 Bowtie bowtie [options]* <ebwt> {- 1 <m1> - 2 <m2> <r> <s>} [<hit>] Exons

32 Tophat tophat [options]* <genome_index_base> <reads1_1[,...,readsn_1]> [reads1_2,...readsn_2] Exons

33 Tophat tophat [options]* <genome_index_base> <reads1_1[,...,readsn_1]> [reads1_2,...readsn_2] Exons isoforms 2 3

34 Tophat TopHat is a splice- aware RNA- seq read aligner Requires a reference genome Breaks reads into pieces, uses bow,e aligner to first align these pieces Then extends alignments from these seeds and resolves exon edges (splice junc,ons) Trapnell et al. 2009

35 Cufflinks Trapnell et al. Nature Biotechnology 28, (2010)

36 Integrative Genome Viewer (IGV) Cytoband Track Names Genomic Coordinates Data Panel Annotation Heatmap Genome Features

37 IGV

38 IGV