Supplementary Figure 1. Fast read-mapping algorithm of BrowserGenome.

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1 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 the genome position of the first occurrence of every possible 12-mer sequence. Therefore, this table counts 4 12 entries and occupies 67 MB of RAM (4 bytes per entry). A second table called a Jump table for every genomic 12-mer position enlists the position wherein the genome the same 12-mer is found the next time. Its number of rows equals the genome length divided by 12. As such, for the human genome 1.1 GB of RAM are occupied (4 bytes per entry). (b) Fast sequence search: From a 25-mer search sequence, 12 overlapping 12-mers are extracted (left panel). For each 12-mer, all genomic occurrences are retrieved by looking them up in the Hook table and then iterating through the Jump table (right panel). At every 12-mer occurrence in the genome, the whole 25-mer search sequence is locally matched to the genome (100% identities, no gaps allowed).

2 Supplementary Figure 2

3 Fast visualization and gene-counting algorithms of BrowserGenome. (a) For visualization of hits in an exemplary viewing range spanning from position 50 to position 80, an unsorted hit list has to be scanned from top to bottom in order to filter visible hits (left panel). BrowserGenome instead uses sorted hit lists (right panel), which allow the localization of only the first and the last hit entry. Localization works in O(log n) time by the algorithm described in Supplementary Note 1. Single comparison operations are color-coded in blue (equal or greater than target) and green (smaller than target). (b) For counting the hit numbers in annotated exonic regions at a genome-wide scale, a naive algorithm would require testing all hit positions for being included in all annotated exons, which would be computationally intense (left panel). BrowserGenome therefore makes use of both sorted exon and hit lists (right panel), which allows genome-wide exon hit counting in seconds by the algorithm detailed in Supplementary Note 1.

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5 Supplementary Figure 3 RNA-seq data mapping performance comparison between STAR and BrowserGenome.org. (a) The RNA-seq test data set ENCFF000DPK containing 26,642,287 raw deep-sequencing reads retrieved from human HepG2 cells was downloaded from encodeproject.org and was analyzed on two different computers using STAR 2.4.2a or BrowserGenome.org. (b) Correlation of gene expression quantification results from STAR and BrowserGenome.org using the same data set. Plotted are the absolute numbers of reads mapped to individual genes on a semi-logarithmic scale with added jitter. The Pearson correlation coefficient R was calculated after jitter addition and logarithmization. Supplementary Figure 4 Correlation of transcript-quantification results of STAR and BrowserGenome with nanostring quantification data. (a,b) ENCODE raw RNA-seq data set ENCFF000DPK was analyzed using STAR (a) or BrowserGenome.org (b) using default parameters. ncounter data of 52 exemplary genes were retrieved from ref. 3. Plotted are the decadic logarithms of the ncounter counts (x-axis) or RPKM values (y-axis) incremented by 10 and 0.1, respectively, in order to omit infinite numbers. Pearson correlation coefficients R were calculated after logarithmization.

6 Supplementary Figure 5 Feature comparison of BrowserGenome with three established graphics-based RNA-seq data-evaluation software tools. (a) Current versions of Galaxy, CLC genomics workbench, and Chipster were compared to BrowserGenome with regard to the features given in the first column.

7 Supplementary Note 1 Algorithms Time-optimized sequence read mapping For fast short read mapping, BrowserGenome uses q-gram indexing algorithms 4,5 similar to that of UCSC BLAT 6 ( BrowserGenome generates a 12-mer index of the genome in the form of two tables: A hook table contains the genomic position of the first occurrence of any possible 12-mer sequence (4 12 =16,777, mer sequences), and a jump table contains the distance from a given position in the genome that one has to jump to find the same 12-mer again (Supplementary Fig. 1a, table length = genome length / 12). Indexing of the human genome takes less than 20 seconds on a standard computer. The convention used for converting DNA sequences to binary data is adapted from the UCSC.2bit file specification ( When BrowserGenome reads raw deep sequencing data from a FASTQ file, it optionally filters the data for a given barcode sequence at a user-defined position. From every read, a 25- mer word is extracted at a user-defined position. 12 interlacing 12-mer seed sequences are extracted from that 25-mer word (Supplementary Fig. 1b, left panel). Every 12-mer is allocated in the genome using the hook table. The jump table is iteratively used to spot all other 12-mer occurrences in the genome (Supplementary Fig. 1b, right panel). Each 12-mer location in the genome at which the whole 25-mer word matches (100% identities, no gaps allowed) is stored in a hit list. After the end of the genome has been reached, the same procedure is repeated with the reverse complement sequence of the 25-mer word, with the results being added to the hit list. Finally, a random hit is picked from the hit list without inherent strand bias. In order to cope with mismatches due to read errors, SNPs, or exon-exon junctions, BrowserGenome optionally extracts a second non-overlapping 25-mer word from reads which have failed to be mapped, and repeats the mapping process once. Time-optimized visualization Read mapping results are stored in RAM as a binary hit table containing the genomic hit position, strand, and length, occupying 5 bytes per hit. Pre-sorting the hit table by genomic position allows sampling of hit data within a given viewing range in O(log n) time by the following algorithm (Supplementary Fig. 2a, right panel): Only the first and the last hit within the viewing range have to be identified, since all hits in between qualify for visualization

8 automatically. To find the nearest hit to the start or end position of the viewing range, the algorithm compares that position to the genome position of the hit in the middle of the hit table. If the hit position is smaller, the algorithm moves down in the hit table and repeats the comparison, while otherwise it moves up. For the first move, the moving offset is a fourth of the hit table length and decreases by a factor of two with every move. Every table position can be reached by log n moves. To make scrolling and zooming appear smooth, the rendering must be performed with >30 Hz. To achieve that for hit tables with millions of rows, only 5000 random hits are sampled within the viewing range by iterating through the hit table using an iterator adjusted accordingly. GENCODE gene annotations were obtained from and are visualized by arrows indicating the genomic orientation of a gene's open reading frame. Time-optimized transcript counting For efficiently evaluating the number of sequencing hits per annotated transcript, the table containing annotated exon positions is also sorted by genomic position, which reduces time consumption from O(n 2 ) to O(n) (Supplementary Fig. 2b). The algorithm starts with the first entry of the hit table and the first entry of the exon table and assesses if this hit can be found within the exon range. If so, a hit counter for the exon is incremented, while otherwise the algorithm moves one step further either in the hit table or in the exon table, depending on whether the last hit position or the last exon position was smaller. When the end of both tables has been reached, exon hit counts are aggregated gene-wise. Optionally, hits can be filtered for correct orientation in the genome. Also, hit numbers per gene can be normalized to the total number of genomic hits per sample and to the length of the longest combined transcript isoform.

9 Supplementary Note 2 Unser manual for BrowserGenome.org BrowserGenome.org was successfully tested on Mozilla Firefox 37, Google Chrome 39, and Apple Safari Choosing the correct genome annotation - In the top menu, click the Genome tab - Select the human or mouse genome, or load a custom genome model in BrowGenModel format (see below) 2. Navigating through the genome - The genome is shown as a circular graph with gene density being visualized eccentrically - Use mouse dragging to turn the genome circle and mouse scrolling (e.g. two fingers on a track pad) to zoom in and out of the genome - To navigate to a genomic locus of interest, enter a gene symbol (official HGNC nomenclature) into the search filed on the bottom left and press enter. Alternatively, chromosome coordinates in the format Chr1: can be used - At highest zoom level, individual genome bases are shown if the raw genome file has been loaded before as described in the following paragraph 3. Map raw deep sequencing data: - Once download the raw genome file (hg38.2bit for the human genome or mm10.2bit for the mouse genome) from UCSC using the links provided below and save them on your hard drive. The links are: In the top menu, click Map deep sequencing data - Click Load genome sequence and select the genome file you have downloaded to your hard drive. The progress of loading the genome file is displayed in the bottom right corner - Choose the FASTQ file containing your raw sequencing data - Choose a name for your sample - If an internal barcode was used, enter its position and sequence - Enter at what position in your reads the 25-mer mapping should start (0 in most cases)

10 - Select if you allow the mapping of a second downstream 25-mer in case of failed mappings - If you have 23 GB of free RAM available on your computer, choosing the 23 GB option will speed up the mapping process roughly 10-fold - Start the read mapping by clicking START. The progress of the read mapping is displayed in the bottom right corner. Typically, 18 million reads will be mapped per hour in normal RAM mode - In order to stop the read mapping before completion, click STOP (The mapping results up to this point will still be processed) 4. Manage read mapping tracks - When the read mapping is finished, the result is listed in the hit track list on the bottom right; the hit density is displayed as an inner circle of the genome graph - Navigate the genome as described in (1.) to inspect the hit densities of hit tracks - Save the read mapping data in a BrowserGenome specific data format by clicking Save next to the entry in the hit track list. The standard file extension is BrowGen. Optionally SAM files can be saved to provide compatibility with other tools - Load existing read mapping data in BrowGen or SAM format by clicking Load file in the hit track list. Example data can be loaded by clicking in the Load reference button above the hit track list. - Remove hit tracks from the list by clicking Unload. This will not delete the corresponding BrowGen data file you had saved earlier. - Save the hit tracks in SVG vector graphics format by clicking the clipboard icon on the middle right of the screen. 5. Quantify gene expression - In the top menu, click Quantify - If a strand-specific sequencing method was used, activate the option: Only count hits on the correct strand - Choose if normalization to total hit numbers is required - Choose if normalization to RNA length is required - Click EXPORT. In the opened download dialog, choose a location to save the results in the format of a tab-delimited text file - Instead of saving the result to the hard drive, you can open it directly with Microsoft Excel or similar software in order to sort or evaluate the data - If normalization to total hit numbers and to RNA length is activated (the default setting), the results are RPKM values (reads per kilo base RNA per million mapped reads)

11 6. Generate a custom genome model - In the top menu, click the Genome tab - In the section Create custom genome model, enter the species name and optionally modify the chromosomes to be included and their order - Choose any 2bit genome sequence file to extract chromosome sizes from - Optionally choose to exclude annotations not marked as KNOWN, marked as read-through transcripts, or marked as confidence level 3 transcripts - Optionally choose to import Ensembl gene IDs instead of gene symbols - When finished, import a GENCODE gene model in GTF format. This can take several minutes to complete. Accept all alerts by clicking OK. - When completed, save the resulting genome model in BrowGenModel format to your hard drive by clicking SAVE.

12 Supplementary Note 3 Reference documentation for the BrowserGenome.js programming library BrowserGenome.js is a JavaScript programming library that allows to incorporate into 3rd party HTML websites functions related to handling genome sequence data, loading reference annotations, indexing genome data, and fast DNA sequence searches. In the following section, the process of incorporating BrowserGenome.js into an HTML website is explained in detail. 1) Including the BrowserGenome.js library in an HTML website - Copy the BrowserGenome.js file to a public folder on your web server - include the following line of code in the body section of the web site: <script src="browsergenome.js"></script> 2) Loading a genome file - In your HTML website, include a file input object for accessing the.2bit genome file <input type="file" id="genomefile"/> - Define the BrowserGenome function LoadGenome() as input callback function <script> document.getelementbyid('genomefile'). </script> addeventlistener('change', LoadGenome, false); - Guide the user of your website to download the raw genome file in.2bit format - Request the user to select the local genome file in the file input element 3) Indexing a genome for fast sequence searches In your JavaScript code, call: GenerateIndex("");

13 Optionally, a callback function can be included in brackets to be executed when the index is ready. 4) Search a given DNA sequence In your JavaScript code, call: var hitnum = SEARCH ("GATCGATCGATCGATCGATCGATC"); The input must be capital letters. The return value will be the number of perfect matches or -1 when failed. The hit positions will be contained in the non-typed arrays: SEARCH_hitpos[0..hitnum-1] SEARCH_hitlen[0..hitnum-1] 5) Pick a random hit from the table of hit positions In your JavaScript code, call: var pick = Math.floor(Math.random()*hitnum); var hitpos = SEARCH_hitpos[pick]; var hitlen = SEARCH_hitlen[pick]; 6) Retrieve the genomic sequence at a given position In your JavaScript code, call: var sequence = getgatc(hitpos, 100); The return value will be a sequence string in capital letters.

14 SUPPLEMENTARY REFERENCES 1. Consortium, E.P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, (2012). 2. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, (2013). 3. Steijger, T. et al. Assessment of transcript reconstruction methods for RNA-seq. Nature methods 10, (2013). 4. Ukkonen, E. Approximate string-matching with q-grams and maximal matches. Theoretical computer science 92, (1992). 5. Burkhardt, S. et al. in Proceedings of the third annual international conference on Computational molecular biology (ACM, Lyon, France; 1999). 6. Kent, W.J. et al. The human genome browser at UCSC. Genome research 12, (2002).