SAM/BAM summarizing and processing
Many of these involve samtools
Get the official SAM/BAM file format description
All fields in a SAM/BAM file are explained in the Sequence Alignment/Map Format Specification.
Differences between SAM and BAM files
- A BAM file is a binary version of a SAM file.
- Both contain identical information about reads and their mapping.
- A BAM file requires a header but a SAM file may not have one. (Use 'samtools view -h reads.bam' to print the header with the mapped reads.)
- Many operations (such as sorting and indexing) work only on BAM files.
- For almost any application that requires SAM input, this can be created on the fly from a BAM file (using 'samtools view reads.bam |').
- BAM files take up much less space than SAM files.
- For archiving purposes, keep only the BAM file. The SAM file can easily be regenerated (if ever needed).
Convert, sort, and/or index
# Convert SAM to BAM: samtools view -bS -o foo.bam foo.sam
# Convert BAM to SAM: samtools view -h -o foo.sam foo.bam
# Sort BAM file (where ".bam" is added to "foo.sorted") samtools sort foo.bam -T foo > foo.sorted.bam
# Index a sorted BAM file (which creates foo.sorted.bam.bai): samtools index foo.sorted.bam # Both foo.sorted.bam and foo.sorted.bam.bai are needed for visualization.
All three steps (SAM=>BAM, sorting, and indexing) can be merged into one command. See
/nfs/BaRC_Public/BaRC_code/Perl/SAM_to_BAM_sort_index/SAM_to_BAM_sort_index.pl # Or on a folder of SAM files for samFile in `/bin/ls *.sam`; do bsub /nfs/BaRC_Public/BaRC_code/Perl/SAM_to_BAM_sort_index/SAM_to_BAM_sort_index.pl $samFile ; done
Modify a BAM file into another BAM file
In many cases, there's no need to create an intermediate SAM file.
#to extract selected (mapped to chrM) reads: samtools index accepted_hits.bam # index file required if you want to select a genome region (like chrM) samtools view -h accepted_hits.bam chrM | samtools view -bS - > accepted_hits.chrM_only.bam #We need to keep the header to convert back to BAM (hence the '-h' with 'samtools view').
#extract multiple regions (e.g., chromosomes) into a new bam file samtools view -bh -L chromInfo.bed alignment.bam > alignment_chr1_3.bam #where chromInfo.bed is a bed file, e.g., chr1 1 195471971 chr2 1 182113224 chr3 1 160039680
#rename header, e.g., use only chr1 to chr3 like above samtools reheader newHeader.txt alignment_chr1_3.bam> alignment_chr1_3.newHeader.bam #where newheader.txt file is, @HD VN:1.3 SO:coordinate @SQ SN:chr1 LN:195471971 @SQ SN:chr2 LN:182113224 @SQ SN:chr3 LN:160039680 @PG ID:bwa PN:bwa VN:0.7.12-r1039 CL:bwa ...
Count the number of mapped reads
samtools flagstat mapped_unmapped.bam > mapped_unmapped.flagstat.txt
Count the number of mapped reads by chromosome
# Method 1 (all chromosomes) # 1 - Index the BAM file: samtools index mapped_reads.bam # 2 - Get index statistics (including the number of mapped reads in the third column: samtools idxstats mapped_reads.bam
# Method 2 (one chromosome at a time, for example, chr2) samtools view mapped_reads.bam chr2 | cut -f 1 | sort -u | wc -l
Remove unmapped reads
samtools view -h -F 4 mapped_unmapped.bam | samtools view -bS > mapped_only.bam
Extract unmapped reads
samtools view -h -f 4 mapped_unmapped.bam | samtools view -bS > unmapped_only.bam
Keep only properly paired reads
samtools view -h -f 2 mapped_unmapped.bam | samtools view -bS > mapped.properly_paired.bam
Extract spliced reads
samtools view -h All_reads.bam | awk '$0 ~ /^@/ || $6 ~ /N/' | samtools view -bS > Spliced_reads.bam
How many multiple/uniquely mapped reads are in a bam/sam file?
bam_stat.py -i mapped_reads.bam >& bam_stat.out.txt
Get only uniquely mapped reads from sam/bam files
# For sam/bam files generated by bowtie2: grep -v "XS:i" | grep "AS:i" All.sam >| Uniquely_mapped.sam samtools view -h All.bam | grep -E -v "XS:i" | grep -E "@|AS:i" | samtools view -b - >| Uniquely_mapped.bam # For any bam files samtools view -b -q 10 All_reads.bam > Uniquely_mapped.bam
Remove or mark duplicate reads in bam files
# Using Picard tools, mark duplicates only, this is the default /pathtojava/java -jar /path_to_picard-tools/picard.jar MarkDuplicates I=input.bam O=ouput.marked_duplicates.bam \ M=ouputFileWithStats_metrics.txt REMOVE_DUPLICATES=FALSE # Using Picard tools, remove duplicates /pathtojava/java -jar /path_to_picard-tools/picard.jar MarkDuplicates I=input.bam O=ouput.marked_duplicates.bam \ M=ouputFileWithStats_metrics.txt REMOVE_DUPLICATES=TRUE
View alignment with samtools
# -e: change identical bases to '=' samtools view -b accepted_hits.bam | samtools fillmd -e - /nfs/genomes/mouse_mm10_dec_11_no_random/fasta_whole_genome/mm10.fa | more
Get a list of multi-mapped reads, including the number of times each one was mapped
Tophat/bowtie mappers create the tag NH:i:XXX where XXX is the number of times the read has mapped.
bsub "samtools view accepted_hits.bam | grep -v NH:i:1 | perl -pe 's/AS.+(NH:i:\d+)/\$1/' | cut -f1,10,12 | perl -pe 's/NH:i://' | sort -u -k3,3nr > Multi-mapped.sorted.txt" # Output format: # read_ID<tab>read<tab>number times mapped
SAM flag explanation
- An explanation of all flags is found in the Sequence Alignment/Map Format Specification.
- Converting a flag into its components may be easiest with the Picard Decoding SAM flags tool.
Split by strand by matched strand
# input: accepted_hits.bam # output: accepted_hits_negStrand.bam: mapped to negative strand # accepted_hits_posStrand.bam: mapped to positive strand bsub "samtools view -f 16 -b accepted_hits.bam >| accepted_hits_negStrand.bam" bsub "samtools view -F 16 -b accepted_hits.bam >| accepted_hits_posStrand.bam"
Split reads by pair
# input: accepted_hits.bam # output: 1st pair: accepted_hits_1stPair.bam # 2nd pair: accepted_hits_2ndPair.bam bsub "samtools view -b -f 0x0040 accepted_hits.bam > accepted_hits_1stPair.bam" bsub "samtools view -b -F 0x0040 accepted_hits.bam > accepted_hits_2ndPair.bam"
Print consensus sequence of mapped reads
samtools mpileup -uf ref_genome.fa Mapped_reads.bam >| Mapped_reads.variants.bcf bcftools call -O v -c Mapped_reads.variants.bcf | vcfutils.pl vcf2fq -d 2 >| Mapped_reads.consensus.fq
Convert BAM to BED format
This can be helpful for genome browser viewing, as features in a BAM file are only visible when the browser is zoomed in enough, whereas BED features are visible at any scale.
# Use bam2bed from the bedtools suite # Run 'bam2bed -h' to get all options bam2bed < Mapped_reads.bam > Mapped_reads.bed
Convert BAM back to fastq format
bamToFastq is part of the bedtools suite.
# Single-end reads bamToFastq -i Reads.bam -fq Reads.fq # Paired-end reads bamToFastq -i Read_pairs.bam -fq Reads.R1.fq -fq2 Reads.R2.fq
Get genome coverage of DNA reads
One way to do this is with a BAM QC analysis using the QualiMap package.
qualimap bamqc -bam Mapped_DNA_reads.bam -outdir qualimap_output -outformat PDF:HTML # Command-line execution may require disabling X-windows bsub "unset DISPLAY; qualimap bamqc -bam Mapped_DNA_reads.bam -outdir qualimap_output -outformat PDF:HTML"
For genome coverage, search for "mean coverageData" in the output file: qualimap_output/genome_results.txt This file includes lots of other details too.