Barcode Sequence Alignment and Statistical Analysis (Barcas) tool

Transcription

1 Barcode Sequence Alignment and Statistical Analysis (Barcas) tool Mun, Jihyeob and Kim, Seon-Young Korea Research Institute of Bioscience and Biotechnology

2 Barcode-Sequencing Ø Genome-wide screening method based on sequencing the counts of tens of thousands of individual tags (barcodes) for each gene for a given condition Ø Originally developed as yeast deletion libraries such as Saccharomyces cerevisiae and Schizosaccharomyces pombe Ø Now applied for genome-wide sirna or shrna screening to measure the effects of knock-down of genes Ø Or, using CRISPR-Cas9, applied for genome-wide sgrna screening for the effects of gene knock-out 2

3 Examples of genome-wide barcode-sequencing libraries Contents Organism # of genes # of barcodes References Yeast deletion consortium S. cerevisiae 6,343 2 (UP and DN) www-sequence.stanford.edu/group/ Bioneer pombe collection S. pombe 4,836 2 (UP and DN) MISSION shrna (human) H. sapiens 20, ,696 shrna MISSION shrna (human) M. musculus 21, ,072 shrna TRC1 (human) shrna H. sapiens 16,019 80,717 shrna TRC1 (mouse) shrna M. musculus 15,960 77,819 shrna Human DECIPHER (shrna) H. sapiens 15, shrnas Mouse DECIPHER (shrna) M. musculus 9, shrnas Cellecta Genome-wide shrna H. sapiens 19,276 8 shrnas Cellecta Genome-wide CRISPR H. sapiens 19,001 8 sgrnas Human GeCKO v2 H. sapiens 19, ,411 sgrna Mouse GeCKO v2 M. musculus 20, ,209 sgrna Mouse genome-wide v1 (yusa) M. musculus 19,150 87,897 sgrna Oxford fly Drosophila 13,501 40,279 sgrna CRISPRa H. sapiens 15, ,810 sgrna CRISPRi H. sapiens 11, ,421 sgrna 3

4 Workflow : barcoded yeast deletion strains 4

5 Workflow : genome-wide shrna screening 5

6 Basic format of barcode-seq data MID (Multiplexing Index, 4-6 bp) Universal Primer (20-25 bp) Barcode (20-30 bp) 6

7 Steps of barcode-seq data analysis Pre-processing and QC Multiplex Index (4-6 bp) Universal Primer (20-bp) Barcode (20-30 bp) Trim index Trim primer Map and count each TAG Visualization Statistical Analyses Normalization sample1 Sample2 sample3 tag tag tag tag

8 Current tools and methods for barcode-seq data analysis Tool (or method) QC Normal ization Statistical Analysis Visuali zation Software format Barcas O O O O O Java GUI Mun 2016 BMC Bioinfo Barcode Deconvoluter Preprocessing BiNGS!LSseq & edger O X X X X Windows or Mac GUI Ref. software O O O O X R package Kim 2012 Method Mol Biol edger O X O O X R package Dai 2014 F1000 Res HiTSelect X X X Multi-objective ranking O Matlab runtime MAGeCK O O O O X Python, C source code MAGeCK- VISPR O O O Robust rank aggregation RIGER X X X RNAi Gene Enrichment Ranking RSA X X X Iterative hypergeometric P- value Diaz 2015 Nuc Acids Res Li 2014 Genome Bio O Python script Li 2015 Genome Bio O GENE-E (=> Morpheus) Java GUI X Windows GUI (C#), R, Perl Luo 2008 PNAS Konig 2007 Nat Methods ScreenBEAM X X X Pooled scoring X R package Yu 2015 Bioinformatics shalign & shrnaseq O O O O X Perl and R script Sims 2011 Genome Bio 8

9 Barcas (Barcode sequence Alignment and Statistical Analysis) - Barcas is an all-in-one program for the analysis of multiplexed barcode sequencing (barcode-seq) data - Available at Input: Barcode-seq data Genome-wide shrnas (Cellecta, TRC, Sigmaaldrich, etc) Genome-wide sgrnas (Addgene, Cellecta, etc) barcoded yeast deletion strains: S. cerevisae or S. pombe Ø Preprocessing & Mapping Filtering, trimming, and mapping with mismatches and indels Ø Quality Control (of barcodes and samples) Ø Normalization Ø Statistical Analysis Two-condition comparison, multiple time points. Ø Visualization Various graphs and heatmap 9

10 All in one package with user-friendly GUI Step 1: Pre-processing & Mapping Step 2: QC of data quality Step 3: Design experiment Step 4: Statistical analysis 10

11 Step 1: Data preprocessing and mapping Ø De-multiplexing and trimming (universal primers) Ø Mapping with imperfect matches (mismatches and indels) Ø Searching for individual tag sequences 11

12 Step 2: Data quality evaluation Ø Sequence level: overall sequence quality Ø Sample level: mapping counts and percentage, etc Ø Barcode (or tag) level: mapping counts and percentage, etc 12

13 Step 3: Experimental design Ø Comparison of two conditions Ø Across several different time points 13

14 Step 4: Statistical analysis and Visualization Ø Calculates z-score and p-value for each barcode Ø Ranks each barcode by z-score Ø Plots z-score graph Ø Plots time dependent intensity heat-map Ø Allows searching for individual target gene 14

15 Novel functions of Barcas for data pre-processing and QC Ø Flexible mapping with support for both substitution s and indels Ø Detection of erroneous barcodes in the library Ø Checking similarity among barcodes in the library collection 15

16 Existing tools for data preprocessing Name Mismatches Shifts of the position BiNGS!LSseq shalign Indel Backend tool O X X bowtie O X X Perl script (or bowtie) edger O O X edger Barcas O O O Trie data structure Ref. Kim (2012) Methods Mol Bio Sims (2011) Genome Bio Dai (2014) F1000Res Mun (2016) BMC Bioinfo Original barcode Perfect match Mismatches Position shift Indel MID Universal Primer Barcode (shrna) TCAAAGATAGTCACGCGACCTCATCGACGAGCTACC TCAAAGATAGTCACGCGACCTCATCGACGAGCTACC TCAAAGATAGTCACGCGACCTCATCGACGAGCTACC TCAAAGATAGTCACGCGACC-ATCGACGAGCTACC TCAAAGATAGTCACGCGACCTCATCGA--AGCTACC 16

17 Algorithm : List based Maximum time : N * M (N: read count, M: reference count) read AGCT Library reference CGCT GCCAA TTAG TCAGT GCAG TTAT AGCT Trie data structure Ø Data structure based on prefix tree Ø Useful data structure to store a dynamic set or associate array in which the keys are usually strings Ø More efficient than hash table (or dictionary) or lists in terms of look-up speed an d memory 1:M sequence matching processing 1:1 sequence matching processing Algorithm : Tree based read AGCT Maximum time : N (N: read count) Library reference root A T G C G C T T T A G C A G A G C C A G C T T A

18 1. Data structure of Barcas for mapping - Based on trie data structure, Barcas supports imperfect matching allowing mismatches, base shifting and indels - Dynamic sequence lengths - Dynamic start positions 18

19 Comparison of speed and mapping rate of barcas with bowtie and edger package of R Data 215 million reads were mapped to 4,832 heterozygous diploid deletion strains in S. pombe. 45-bp sequences were used as barcode library. Option Result Barcas was 1.7 times faster than bowtie and 13 times faster than edger. Owing to indel mapping, Barcas mapped at least 8-12% more than the other two programs.

20 2. Detection of erroneous barcodes from the genome-wide barcode library Ø We are likely to assume that barcode sequences in the li brary are perfectly error-free from the original design Ø However, errors can creep in the barcodes during many steps including barcode synthesis, random mutations during library maintenance, erroneous incorporation of barcodes into the genome in case of yeast strains. 20

21 Erroneous barcodes in the yeast library Eason et al (2004) Characterization of synthetic DNA bar codes in Saccharomyces cerevisiae gene-deletion strains PNAS 101(30): Smith et al (2009) Quantitative phenotyping via deep barcode sequencing Genome Res 19: # correct by Smith % correct by Smith # correct by Easton % correct by Easton U1 UpTag U2 D2 DnTag D1 4,242 4,369 4,045 4,207 4,320 3, % 82.5% 82.9% 80.9% 83.1% 83.7% ,764 4,057 4,343 3,807 4, % 71.1% 83.2% 83.5% 73.2% 88.7% % Agreed 86% 84.4% 89.2% 92.6% 85.1% 92% 21

22 A simple method to detect erroneous barcodes Measure the amount of gains in count between perfect match only and (PM + MM) Original design Dominant Perfect Match with minor Mismatches ACTGACTGACTGACTGACTG Counts Perfect ACTGACTGACTGACTGACTG 50,000 Mismatch 1 ACTGACTGACTGACTGCCTG 10 Mismatch 2 ACTCACTGACTGACTGACTG 9 Mismatch 3 ACTGACAGACTGACTGACTG 20 Mismatch 4 ACTGACTGACTTACTGACTG 3 Mismatch 5 AGTGACTGACTGACTGACTG 7 Mismatch 6 ACTGACTGACTGACTGTCTG 12 Mismatch 7 ACTGACTGACTAACTGACTG 5 PM only 50,000 PM + MM 50,065 Gain 50,565/50,000 = 1.013% 0.13% gain One dominant Mismatch with minor Perfect Match and other Mismatches Original design ACTGACTGACTGACTGACTG Counts Perfect ACTGACTGACTGACTGACTG 200 Mismatch 1 ACTGACTGACTGACTGCCTG 40,000 Mismatch 2 ACTCACTGACTGACTGACTG 11 Mismatch 3 ACTGACAGACTGACTGACTG 12 Mismatch 4 ACTGACTGACTTACTGACTG 3 Mismatch 5 AGTGACTGACTGACTGACTG 12 Mismatch 6 ACTGACTGACTGACTGTCTG 9 Mismatch 7 ACTGACTGACTAACTGACTG 5 PM only 20 PM + MM 40,071 Gain 40,071/200 = % 200% gain

23 Detection of erroneous barcodes Ø Library : 1,230 shrna sequences of TRC library. Ø Data : Control samples in neuroepithelial (NE), early radial glial (ERG) and mid radial glial (MRG) Ø We found 25 erroneous barcodes (2.03%). Ziller,MJ. et al., Nature 2015, 518,

24 Detection of erroneous barcodes (TRC) Gene ID Original sequence Major mapped (Two mismatch/indels) PM count MM count PBX2 TRCN ATACTCCCACTTGCAACTATT ATACTCCCACTTGTAACTATT 10,785 34,084 SKI TRCN GAATCTGCCACTCTCAGAATA -AATCTGCCACTCTCAGAATA 14 5,935 TERF2IP TRCN GAGAGTTCTTGCATTGGAACT -AGAGTTCTTGCATTGGAACT 4 1,244 SKI TRCN GATCGAAGACCTGCAGGTGAA -ATCGAAGACCTGCAGGTGAA MYC TRCN GAATGTCAAGAGGCGAACACA -AATGTCAAGAGGCGAACACA JDP2 TRCN CGGGAGAAGAACAAAGTCGCA CGGGAGAAGAACAAAAACGCA TFAP2B TRCN CGGTTCTTTCGAGTTTAGTAA CGGTTCTTTTGAGTTTTGTAA NFFKB TRCN CAGGGAGGTTGCATCATTGTT CAGGGAGGGTGCATCATTGTT KLF13 TRCN CGGGCGAGAAGAAGTTCAGCT CGGGCGAGAAGAAGTTCATGGT

25 3. Check for sequence similarity among barcodes in a reference Ø Erroneous barcodes can potentially be generated during the production of many barcodes. Ø If two barcodes were designed similarly (i.e only 1 bp difference) and mutations or sequencing errors occur, then it will be hard to distinguish errors from true differences. Ø Thus, barcodes originally designed to be similar should be identified (and flagged) in advance. Ø For this purpose, Barcas allows checking of sequence similarity among barcode sequences. 25

26 Library reference QC Tested public library sets (11) Screen Library Date Species Module TRC 05/Apr/11 Barcode length Barcode count Gene count 21-bp 61,621 15,435 shrna sgrna Human Module1 18-bp 27,500 5,046 Cellecta 15/Feb/12 Module2 18-bp 27,500 5,421 Module3 18-bp 27,500 4,923 yusa Mouse 19-bp 87,437 19,149 CeCKOv2 09/Mar/15 Human Library A 20-bp 63,950 21,669 Library B 20-bp 56,869 19,834 Mouse Library A 20-bp 65,959 22,486 Library B 20-bp 61,139 21,263 Deletion mutant strains Heterozyg ous diploid Saccharomyces cerevisiae Schizosaccharomyces pombe 20-bp 20-bp 6,318/UP 6,126/DN 4,832/UP 4,832/DN 6,131 4,832 26

27 Library reference QC Barcode counts having similar pairs within one base Library Static sequence length comparison Dynamic sequence length Comparison (indels) GeCKOv2.Human.A 517 (0.81%) 538 (0.84%) GeCKOv2.Human.B 437 (0.77%) 441 (0.78%) GeCKOv2.Mouse.A 736 (1.12%) 755 (1.14%) GeCKOv2.Mouse.B 850 (1.39%) 860 (1.41%) yusa 517 (0.59%) 3,944 (4.51%) Cellecta.Human.M1 0 (0 %) 412 (1.5%) Cellecta.Human.M2 0 (0 %) 398 (1.45%) Cellecta.Human.M3 0 (0 %) 410 (1.49%) TRC 790 (1.28%) 1,909 (3.10%) S. cerevisiae 0 (0 %) 0 (0 %) S. pombe 0 (0 %) 0 (0 %) 27

28 Conclusions Ø Barcas is an all-in-one software for barcode-seq data analysis with user-friendly interface and a few new useful functions for data pre-processing and quality control of barcode library Ø Future improvements Supports for diverse statistical analyses Sophisticated gene-level summary statistics for shrna and sgrna RSA, RIGER, MAGeCK, HiTSelect, ScreenBEAM, etc Multiple-condition comparison (MAGeCK-VISPR) Utilization of metadata and gene-set level analysis (HiTSelect) Ø We hope Barcas will be useful for many researchers with minimal bioinformatics skills for barcode-seq data analysis 28

29 Thank you for your attention 29

30 Limits of the mapping of edger package 1. Indels in the barcode reads are not supported 2. Only shifts of the barcode positions allowed 3. Mismatches in the MID, universal primers not allowed 4. Indels in the MID and universal primers not allowed Loss of sequences with indels in any of the MID, primers and barcodes Loss of sequences with mismatches in the MID and primers Read format MID Universal Primer Barcode (shrna) Example 1: TRC Library Different primer lengths of universal primers: Forward: 37 bp, reverse 42 bp Example 2: Cellecta library Different MID lengths: From 9 to 17 bp Universal Primer (sense) Barcode (shrna) MID Universal Primer Barcode (shrna) Universal Primer (anti-sense) Barcode (shrna) MID Universal Primer Barcode (shrna) MID Universal Primer Barcode (shrna) 30