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Quality Control and Mapping

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Next-Generaon Sequencing: Quality Control and Mapping BaRC Hot Topics – January 2015 BioinFormacs and ResearcH Compu*ng WHiteHead Ins*tute HKp://barc.wi.mit.edu/Hot_topics/ Outline • Quality control • Preprocessing • Read mapping – Non-spliced alignment – Spliced alignment • Post process the mapped read files – Remove unmapped reads, sort, index etc – Mapping stas*cs 2 Illumina data Format • Fastq Format: /1 or /2 paired-end HKp://en.wikipedia.org/wiki/FASTQ_format @ILLUMINA-F6C19_0048_FC:5:1:12440:1460#0/1 @seq idenfier GTAGAACTGGTACGGACAAGGGGAATCTGACTGTAG seq +ILLUMINA-F6C19_0048_FC:5:1:12440:1460#0/1 +any descrip*on hhhhhhhhhhhghhhhhhhehhhedhhhhfhhhhhh seq quality values Input quali*es Illumina versions --solexa-quals <= 1.2 --phred64 1.3-1.7 --phred33 >= 1.8 3 CHeck read quality with Fastqc (HKp://www.bioinFormacs.babraham.ac.uk/projects/Fastqc/) 1. Run Fastqc to cHeck read quality bsub Fastqc sample.Fastq 2. Open output file: “Fastqc_report.Html” 4 Fastqc report We Have to know the quality encoding to use the appropriate parameter in the mapping step. 5 FastQC: per base sequence quality very good quality calls reasonable quality • C poor quality o n t e n t Red: median blue: mean yellow: 25%, 75% whiskers: 10%, 90% 6 6 Preprocessing tools • Fastx Toolkit (hKp://Hannonlab.csHl.edu/Fastx_toolkit/) – FASTQ/A Trimmer: SHortening reads in a FASTQ or FASTQ files (removing barcodes or noise). – FASTQ Quality Filter: Filters sequences based on quality – FASTQ Quality Trimmer: Trims (cuts) sequences based on quality – FASTQ Masker: Masks nucleo*des with 'N' (or other cHaracter) based on quality (For a complete list go to the link above) • cutadapt to remove adapters (HKps://code.google.com/p/cutadapt/) 7 WHat preprocessing do we need? Flagged Kmer Content: About 100% oF the first Bad quality -> Use six bases are the same sequence -> Use “FASTQ Quality Filter” and/or “FASTQ Quality “FASTQTrimmer” Trimmer” Sequence Count Percentage Possible Source RNA PCR Primer, Index 3 (100% TGGAATTCTCGGGTGCCAAGGAACTCCAGTCACTTAGGCA 7360116 82.88507591015895 over 40bp) GCGAGTGCGGTAGAGGGTAGTGGAATTCTCGGGTGCCAA 541189 6.094535921273932 No Hit G TCGAATTGCCTTTGGGACTGCGAGGCTTTGAGGACGGAAG 291330 3.2807783416601866 No Hit RNA PCR Primer, Index 3 (100% CCTGGAATTCTCGGGTGCCAAGGAACTCCAGTCACTTAGG 210051 2.365464495397192 over 38bp) Overrepresented sequences -> If the over represented sequence is an adapter use “cutadapt” 8 Examples oF preprocessing I hands on exercise Remove reads with lower quality -i: input file -o: output file bsub Fastq_quality_filter -v -q 20 -p 75 -v: report number oF sequences -q 20 the quality value required -i sample.Fastq -o sample_filtered.Fastq -p 75 the percentage oF bases that Have to Have that quality value -q 20 -p 75 -F: First base to keep -l: Last base to keep Trim the reads -i: input file -o: output file # Delete the first 6nt From 5’ -v: report number oF sequences bsub Fastx_trimmer -v -f 7 -l 36 -i sample.Fastq -o sample_trimmed.Fastq 9 Examples oF preprocessing II hands on exercise • Remove adapter/Linker cutadapt # usage bsub " cutadapt -m 20 -b GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGT CTTCTGCTTG sample2.Fastq | Fastx_ar*Facts_filter > sample2_trimFilt.Fastq” -a: Sequence oF an adapter that was ligated to the 3' end 10 -b: Sequence oF an adapter that was ligated to the 5' or 3' end -g: Sequence oF an adapter that was ligated to the 5' end -o: output file name 10 Recommendaon For preprocessing • Treat all the samples the same way. • WatcH out For preprocessing that may result in very different read length in the different samples as that can affect mapping. • If you Have paired-end reads, make sure you s*ll Have both reads oF the pair aer the processing is done. • Run Fastqc on the processed samples to see iF the problem Has been removed. 11 Local genomic files needed For mapping tak: /nfs/genomes/ – Human, mouse, zebrafisH, C.elegans, fly, yeast, etc. – Different genome builds • mm9: mouse_gp_jul_07 • mm10: mouse_mm10_dec_11 – Human_gp_feb_09 vs Human_gp_feb_09_no_random? • Human_gp_feb_09 includes *_random.Fa, *hap*.fa, etc. – Sub directories: • bowe – Bow*e1: *.ebwt – Bow*e2: *.bt2 • Fasta: • Fasta_wHole_genome: all sequences in one file • g: gene models From ReFseq, Ensembl, etc. 12 Mapping I Non-spliced alignment sovware § Used mapping DNA Fragments, i.e. CHIP-seq, SNP calling § Bowe: § bowe 1 vs bowe 2 § For reads >50 bp Bow*e 2 is generally Faster, more sensi*ve, and uses less memory than Bow*e 1. § Bow*e 2 supports gapped alignment, it makes it beKer For snp calling. Bow*e 1 only finds ungapped alignments. § Bow*e 2 supports a "local" alignment mode, in addi*on to the “end-to-end" alignment mode supported by bow*e1. § BWA: § reFer to the BaRC SOP For detailed inFormaon 13 Mapping reads with bow*e2 • Mapping single reads: bow*e2 [op*ons]* -x <bt2-index> -U <r> [-S <output.sam>] bsub bowAe2 --phred64 –x /nfs/genomes/mouse_mm10_dec_11_no_random/bowAe/ mm10 –U DNA.fastq –S DNA.sam • Mapping paired-end reads: bow*e2 [op*ons]* -x <bt2-index> -1 <m1> -2 <m2> [-S < output.sam >] bsub bowAe2 --phred64 –x /nfs/genomes/mouse_mm10_dec_11_no_random/bowAe/mm10 -1 Reads1.fastq -2 Reads2.fastq –S DNA.sam 14 Some important parameters in bow*e2 • ReporAng (deFault) look For mul*ple alignments, report best, with MAPQ OR -k <int> report up to <int> alns per read; MAPQ not meaningFul OR -a/--all report all alignments; very slow, MAPQ not meaningFul • Alignment mode --end-to-end en*re read must align; no clipping (on) OR --local local alignment; ends migHt be sov clipped (off) • -L <int> length oF seed substrings; must be >3 and <32 (deFault=22) • -N <int> max # mismatcHes in seed alignment; can be 0 or 1 (deFault=0) Input quali*es Illumina versions --solexa-quals <= 1.2 --phred64 1.3-1.7 --pHred33 (deFault) >= 1.8 15 Mapping II Spliced alignment sovware § Used iF mapping RNA Fragments § TopHat2 (uses bow*e2) § Star: maps >60 *mes Faster than TopHat2, tends to align more reads to pseudogenes. See barc SOPs 16 Spliced alignment with topHat2 TopHat2 uses bow*e2 to map the reads # single-end reads bsub tophat --solexa1.3-quals --segment-length 20 --no-novel-juncs -G /nfs/genomes/ mouse_mm10_dec_11_no_random/g/mm10_no_random.refseq.gX /nfs/genomes/ mouse_mm10_dec_11_no_random/bowAe/mm10 sample_good_trimmed.fastq # paired-end reads: Add addi*onal Fastq file to the end oF above command. Input quali*es ReFer to bow*e2 mapping slide SHortest length oF a spliced read that can map to one side oF the --segment-length junc*on. deFault:25 --no-novel-juncs Only look at reads across junc*ons in the supplied GFF file -G <GTF file> Map reads to virtual transcriptome (From g file) first. -N max. number oF mismatcHes in a read, deFault is 2 -o/--output-dir deFault = topHat_out --library-type (Fr-unstranded, Fr-firststrand, Fr-secondstrand) -I/--max-intron-length deFault: 500000 17 Op*mize mapping across introns • TopHat deFault parameters are designed For mammalian RNA-seq data. • Reduce “maximum intron length” For non- mammalian organisms -l: deFault is 500,000 Species Max_intron_length yeast 2,484 arabidopsis 11,603 C. elegans 100,913 fly 141,628 18 Hands on Mapping • bowAe2 bsub bowAe2 --phred64 –x /nfs/genomes/ mouse_mm10_dec_11_no_random/bowAe/mm10 –U DNA.fastq –S DNA.sam • tophat bsub tophat --solexa1.3-quals --segment-length 20 -G /nfs/ genomes/mouse_mm10_dec_11_no_random/g/ mm10_no_random.refseq.gX /nfs/genomes/ mouse_mm10_dec_11_no_random/bowAe/mm10 sample_good_trimmed.fastq Note: topHat output file will be: topHat_out/accepted_hits.bam 19 Mapped reads file Formats: SAM/BAM • SAM: Sequence Alignment/Map format. It is a TAB-delimited text Format consis*ng oF a Header sec*on, wHicH is op*onal, and an alignment sec*on. EacH alignment line Has 11 mandatory fields For essen*al alignment inFormaon. • BAM: binary Format. It is mucH smaller than sam. • Bam is needed For viewing in a genome browser. It Has to be sorted and indexed. • To save space you sHould convert mapped files to .bam Format, and delete the .sam file. 20 SAM tools: Set oF tools For manipulang mapped read files TOOL DESCRIPTION samtools view conversion between SAM and BAM files samtools flagstat simple stas*cs on the mapped reads samtools sort sort alignment file samtools index index alignment samtools rmdup remove PCR duplicates samtools displays all the tools available 21 Hands on Convert .sam to .bam Format, sort and index. bsub /nfs/BaRC_Public/BaRC_code/Perl/SAM_to_BAM_sort_index/SAM_to_BAM_sort_index.pl DNA.sam 1. Convert .sam to .bam 2. Sort bam file 3. Index bam file, created a .bai file Delete the .sam file 22 How to get the number oF reads mapped • Bow*e2 prints to STDERR the number oF reads mapped, so you will see iF in the email that you received. • TopHat makes a summary file in the topHat output directory. Head topHat_out/align_summary.txt • Tools: – bam_stat.py -i accepted_hits.bam – samtools flagstat mapped_unmapped.bam • See BaRC SOPs HKp://barcwiki.wi.mit.edu/wiki/SOPs/ miningSAMBAM 23 WHat to look For wHen Few reads mapped? • Reads are not perFectly paired * – Usually occurs aer QC’ing step. Removing low quality reads or adapters creates uneven distribu*on oF reads bsub “/nFs/BaRC_Public/BaRC_code/Perl/cmpFastq/ cmpFastq.pl s_8_1_filtered.Fastq s_8_2_filtered.Fastq” • Reads may Have adapter sequences – Blast top overrepresented sequences in FastQC output – ReFer to the preprocessing steps • Mapping parameters are too stringent * – Increase number oF mismatcHes – Adjust the insert size oF paired-end reads? * ReFer to BaRC SOP For more inFormaon 24 Summary • Quality control – Fastqc • Clean up reads: – Fastx tool kit: Fastq_quality_filter, Fastx_trimmer – Cutadapt • Map reads: – Bowe2 – TopHat2 • Understand the mapped files, and cHeck mapping quality: – Samtools – RSeQC:bam_stat.py 25 BaRC Standard operang procedures HKp://barcwiki.wi.mit.edu/wiki/SOPs 26 ReFerences Fastqc: HKp://www.bioinFormacs.babraham.ac.uk/projects/Fastqc Fastx Toolkit: hKp://Hannonlab.csHl.edu/Fastx_toolkit/ cutadapt: HKps://code.google.com/p/cutadapt Bowe: Langmead B, Trapnell C, Pop M, Salzberg SL.
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    Andrew Nightingale1, Jie luo, Michele Magrane1, Peter McGarvey2, Sandra Orchard1, Maria Martin1, UniProt Consortium1,2,3 1EMBL-European Bioinformatics Institute, Cambridge, UK 2SIB Swiss Institute of Bioinformatics, Geneva, Switzerland 3Protein Information Resource, Georgetown University, Washington DC & University of Delaware, USA Enabling interpretation of protein variation effects with UniProt Introduction Understanding the effect of genetic variants on protein function is crucial to thoroughly understand the role of proteins in disease biology. UniProt aims to support the scientific community, computational biologists and clinical researchers, by providing a comprehensive, high-quality and freely accessible resource of protein sequence and functional information. This includes a comprehensive catalogue of protein altering variation data coupled with information about how these variants affect protein function. UniProt variant data sources Variant data from literature Large-scale variant data Variants are captured from the scientific 1. Imported variant literature and manually reviewed for data is dependent addition to UniProtKB/Swiss-Prot upon exact mapping between the reference proteome Description of disease associated with and genome. genetic variations in a protein TCGA 2. Variant data is imported from a variety of resources Variant data including effects of the variant Database Total Imported Variant Total Unique to complement the on the protein and links to variant 1000Genomes 859,757 81,216 resources ClinVar 183,655 76,218 set of variants COSMIC 184,237 18,863 Category Number ESP 939,238 68,803 captured from the ExAC 4,333,620 2,776,617 Total reviewed variants 79,284 TCGA 1,202,700 920,549 literature Disease-associated variants 30,471 UniProt 80,224 49,971 Total 7,781,431 3,992,437 Number of proteins with variants 12,886 *Represents the number of UniProt variants with a dbSNP identifier Interpretation protein variant effect with UniProt 1.
  • Introduction to Bioinformatics (Elective) – SBB1609

    Introduction to Bioinformatics (Elective) – SBB1609

    SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.