Analysis of barcode sequencing

Transcription

1 Analysis of barcode sequencing Department of Functional Genomics, UST Jihyeob Mun

2 Pooled library screen analysis experience knowledge gene A is a target? High-throughput Simplicity Fail Pooled library screen analysis gene A targeted cell A pool gene C targeted cell gene B targeted cell gene D targeted cell The pool is used to analyze. Success How? 2

3 What is barcode sequencing Barcode sequence - Barcodes are Genome-integrated artificial sequences that specifically mark biological materials, such as cells or genes, with unique sequences. - Library : a set of barcode sequences - The barcodes are sequenced and analyzed by barcode sequencing (barcodeseq). - Barcode-seq is used in several genome-wide screening tools, including shrnas, sgrnas and barcoded yeast deletion strains. 3

4 Workflow : genome-wide shrna screening 4

5 Workflow : barcoded yeast deletion strains 5

6 Limitation of barcode-seq data analysis Previously reported tools are mostly focused on shrna or sgrna screening analysis Until now, error free production of barcode libraries is important issue. (For examples, barcode error, off-target problem, etc.) Genome-wide functional analysis using the Barcode Sequence Alignment and Statistical Analysis (Barcas) tool 6

7 What is Barcas - Barcas (Barcode sequence Alignment and Statistical analysis tool) is a specialized program for the analysis of multiplexed barcode sequencing (barcode-seq) data - input: Barcode-seq data (from shrnas, sgrnas and barcoded yeast deletion strains) 7

8 Analysis pipeline of barcode-seq data Step 1: Data pre-processing Step 2: QC of data Step 3: Design experiment Step 4: Statistical analysis

9 Three novel functions of Barcas - Based on trie data structure, Barcas supports imperfect matching containing mismatches, position shifts and indels (insertion and deletion). - Detection of barcode errors in the library. - Checking similarity between barcodes in the library collection (barcode library QC). 9

10 Feature 1: Trie data structure based imperfect matching 10

11 Previously reported tools for data preprocessing Program Mismatches shifts Indels Dynamic length Backend tool Ref BiNGS!LS-seq O X X X bowtie shalign O X X X Perl script (or bowtie) edger O O X X edger Barcas O O O O java MID Universal Primer Barcode Kim (2012) Methods Mol Bio Sims (2011) Genome Bio Dai (2014) F1000Res Mun (2016) BMC Bioinfo Dynamic sequence length MID Universal Primer Barcode MID Universal Primer Barcode ex) The Cellecta library (shrna) MID from 9-bp to 17-bp. MID Universal Primer Barcode

12 Trie data structure based imperfect matching 1:1 sequence matching processing Algorithm : List based Maximum time : N * M (N: read count, M: library sequence count) 1:M sequence matching processing Algorithm : Trie based Maximum time : N (N: read count) Read TTAG Library sequences AGCT TTAT TTAG TCAGT GCAG Library sequences root A T G C G T C C G C A A A C C GCCAA T T G G G A T CGCT T A 12 A sequence A base

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

14 Feature 2: Detection of barcode errors 14

15 Methods of targeting regions (1/2) Barcoded yeast deletion strains Homologous recombination site shrnas sgrnas When the artificial sequence targets an unexpected region, it is called off-target 15

16 Methods of targeting regions (2/2) Original Design Correct sequence Barcode error high low low high 16 True Off-target Solutions are provided by statistical analysis True Off-target Not yet; It is essential with imperfect matching

17 Detection of barcode errors (1/4) Barcoded yeast deletion strains 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%

18 Detection of barcode errors (2/4) - Library : 1,230 shrna sequences of TRC library. - Data : Control samples in neuroepithelial (NE), early radial glial (ERG) and mid radial glial (MRG) - We found 25 (2.03%) erroneous barcodes (<= 2 bases mismatches or indels). 18 Ziller,MJ. et al., Nature 2015, 518,

19 Detection of barcode errors (3/4) Deletion Mismatch Insertion 19

20 Detection of barcode errors (4/4) A simple method distinguishing barcode errors (PM: perfect matching, IM: imperfect matching) Deletion Mismatch Insertion Dominant PM Barcode error Original GCTGGAGATCCTCAAAGTCAT GAATCTGCCACTCTCAGAATA = Real GCTGGAGATCCTCAAAGTCAT (IM1) AATCTGCCACTCTCAGAATA 20

21 Inclusion of barcode errors Barcas supports an option of filtering barcode errors Barcoded yeast deletion strains include barcode errors shrnas (or sgrnas) filtering barcode errors Except several libraries ex) Cellecta library 21 Why use imperfect matching in shrnas? Increase mapped read counts Consider mutated primers (shifts) Provide additional information

22 Feature 3: Checking similarity between original barcodes 22

23 Library reference QC (1/2) - Barcode errors can potentially be generated during the production of many barcodes. - If some barcodes are designed similarly and mutations or sequencing errors occur, then it is hard to distinguish errors from true differences. - Thus, barcodes originally designed to be similar should be separated in a step of pooling. - For this purpose, Barcas gives notice about sequence similarity between barcodes. 23

24 Library reference QC (2/2) Screen Library Date Species Module Barcode length Barcode count Gene count Reference Static sequence comparison Dynamic sequence comparison TRC 05/Apr/11 21-bp 61,621 15,435 nai/public/ 790 (1.28 %) 1,909 (3.10 %) shrna Human Module1 18-bp 27,500 5,046 0 (0 %) 412 (1.5 %) Cellecta 15/Feb/12 Module2 18-bp 27,500 5, (0 %) 398 (1.45 %) Module3 18-bp 27,500 4,923 0 (0 %) 410 (1.49 %) yusa Mouse 19-bp 87,437 19,149 Koike et al., (0.59 %) 3,944 (4.51 %) sgrna CeCKOv2 09/Mar/15 Human Mouse Library A 20-bp 63,950 21, (0.81 %) 538 (0.84 %) Library B Library A 20-bp 20-bp 56,869 65,959 19,834 22,486 r/libraries/geckov2/ 437 (0.77 %) 736 (1.12 %) 441 (0.78 %) 755 (1.14 %) Library B 20-bp 61,139 21, (1.39 %) 860 (1.41 %) Deletion mutant strains Heterozyg ous diploid Saccharomyces cerevisiae Schizosaccharomyce s pombe 20-bp 20-bp 6,318/UP 6,126/DN 4,832/UP 4,832/DN 6,131 yeast_deletion_project/deletio ns3.html 0 (0 %) 0 (0 %) 4,832 Kim,D.U. et al, (0 %) 0 (0 %) 24

25 Conclusion Barcas is an all-in-one software for barcode-seq data analysis and a few new useful functions for data pre-processing and quality control of barcode library Improvement point Memory usage Trie-data structure consumes more memory as sequence gets longer due to recursive function. Barcas consumes much memory while making middle files (.seqmap) from fastq or fasta in mapping step. For example, Barcas needs about 350 MB memories for uploading Yusa library (19-bp 87,437 barcodes). 25 Statistical analysis Multiple-condition comparison (MAGeCK-VISPR) Utilization of metadata (HiTSelect)

26 Acknowledgement Dr. Seon-Young Kim, Dr. Jong-Lyul Park and Jeong-Hwan Kim Aging Research Center of KRIBB Dong-Uk Kim Chungnam National University Dr. Kwang-Lae Hoe, Dr. 이숙정, Miyoung Nam, 이아름, etc. 26

27 Thank you for listening

28 Static comparison vs. Dynamic comparison Comparison AGCT sequence with ACTA sequence Static comparison 2 bases Based on the same lengths between sequences Static comparison 1 base Based on the length of a specific sequence Input sequence (read) AGCT ACTA Barcode region Other region 28