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module load samtools
cd $SCRATCH/core_ngs/alignment/vibCho
bowtie2 --local -x vibCho/vibCho.O395 -U fq/cholera_rnaseq.fastq.gz 2>aln_local.log | \
  samtools view -b > cholera_rnaseq.local.bam

89006 reads; of these:
  89006 (100.00%) were unpaired; of these:
    27061 (30.40%) aligned 0 times
    33828 (38.01%) aligned exactly 1 time
    28117 (31.59%) aligned >1 times
69.60% overall alignment rate

Exercise #5: BWA-MEM - Human mRNA-seq

After bowtie2 came out with a local alignment option, it wasn't long before bwa developed its own local alignment algorithm called BWA-MEM (for Maximal Exact Matches), implemented by the bwa mem command. bwa mem has the following advantages:

• It incorporates a lot of the simplicity of using bwa with the complexities of local alignment, enabling straightforward alignment of datasets like the mirbase data we just examined
• It can align different portions of a read to different locations on the genome
  • In a total RNA-seq experiment, reads will (at some frequency) span a splice junction themselves
    • or a pair of reads in a paired-end library will fall on either side of a splice junction.
  • We want to be able to align these splice-adjacent reads for many reasons, from accurate transcript quantification to novel fusion transcript discovery.

This exercise will align a human total RNA-seq dataset composed (by design) almost exclusively of reads that cross splice junctions.

A word about real splice-aware aligners

Using BWA mem for RNA-seq alignment is sort of a "poor man's" RNA-seq alignment method. Real splice-aware aligners like tophat2, hisat2 or STAR have more complex algorithms (as shown below) – and take a lot more time!

Image Added

In the transcriptome-aware alignment above, reads that span splice junctions are reported in the SAM file with genomic coordinates that start in the first exon and end in the second exon (the CIGAR string uses the N operator, e.g. 30M1000N60M).

BWA MEM does not know about the exon structore of the genome. But it can align different sub-sections of a read to two different locations, producing two alignment records from one input read (one of the two will be marked as secondary (0x100 flag).

Setup for BWA mem

First set up our working directory for this alignment. Since it takes a long time to build a bwa index for a large genome (here human hg38/GRCh38), we'll use one that the BioITeam maintains in its /work2/projects/BioITeam/ref_genome area.

# Make sure you're in an idev session
idev -m 120 -p normal -A UT-2015-05-18 -N 1 -n 68

# Load the modules we'll need
module load biocontainers
module load bwa
module load samtools

# Copy over the FASTQ data if needed
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*.gz $SCRATCH/core_ngs/alignment/fastq/

# Make a new alignment directory for running these scripts
mkdir -p $SCRATCH/core_ngs/alignment/bwamem
cd $SCRATCH/core_ngs/alignment/bwamem
ln -sf ../fastq
ln -sf /work2/projects/BioITeam/ref_genome/bwa/bwtsw/hg38

Now take a look at bwa mem usage (type bwa mem with no arguments). The most important parameters are the following:

RNA-seq alignment with bwa mem

Based on its help info, this is the structure of the bwa mem command we will use:

bwa mem -M <ref.fa> <reads.fq> > outfile.sam

After performing the setup above, execute the following command in your idev session:

cd $SCRATCH/core_ngs/alignment/bwamem
bwa mem -M hg38/hg38.fa fastq/human_rnaseq.fastq.gz 2>hs_rna.bwamem.log |
  samtools view -b | \
  samtools sort -O BAM -T human_rnaseq.tmp > human_rnaseq.sort.bam

This multi-pipe command performs three steps:

• The bwa mem alignment
  • the program's progress output (on standard error) is redirected to a log file (2>hs_rna.bwamem.log)
  • its alignment records (on standard output) is piped to the next step (conversion to BAM)
• Conversion of bwa mem's SAM output to BAM format
  • recall that the -b option to samtools view says to output in BAM format
• Sorting the BAM file

Because the progress output is being redirected to a log file, we won't see anything until the command completes. Then you should have a human_rnaseq.sort.bam file and an hs_rna.bwamem.log logfile.

Exercise: Compare the number of original FASTQ reads to the number of alignment records.

cd $SCRATCH/core_ngs/alignment/bwamem
zcat ./fastq/human_rnaseq.fastq.gz | wc -l | awk '{print $1/4}'

cd $SCRATCH/core_ngs/alignment/bwamem
samtools flagstat human_rnaseq.sort.bam | tee hs_rnaseq.flagstat.txt

133570 + 0 in total (QC-passed reads + QC-failed reads)
33570 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
133450 + 0 mapped (99.91% : N/A)
0 + 0 paired in sequencing
0 + 0 read1
0 + 0 read2
0 + 0 properly paired (N/A : N/A)
0 + 0 with itself and mate mapped
0 + 0 singletons (N/A : N/A)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5)