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 to bigwig format

This can also be helpful for genome browser viewing. Bigwig files are smaller than BED files.

# Use bamCoverage from the deepTools suite
bamCoverage -b Mapped_reads.bam -o Mapped_reads.bw

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.