More Alignment exercises


Use our summer school reservation (CoreNGS) when submitting batch jobs to get higher priority on the ls6 normal queue.

Request an interactive (idev) node
# Request a 180 minute interactive node on the normal queue using our reservation
idev -m 120 -N 1 -A OTH21164 -r CoreNGS
idev -m 120 -N 1 -A TRA23004 -r CoreNGS

# Request a 120 minute idev node on the development queue 
idev -m 120 -N 1 -A OTH21164 -p development
idev -m 120 -N 1 -A TRA23004 -p development

Submit a batch job
# Using our reservation
sbatch --reseservation=CoreNGS <batch_file>.slurm

Note that the reservation name (CoreNGS) is different from the TACC allocation/project for this class, which is OTH21164.

Exercise #3: PE alignment with BioITeam scripts

Now that you've done everything the hard way, let's see how to do run an alignment pipeline using a BWA alignment script maintained by the BioITeam,  /work/projects/BioITeam/common/script/ Type in the script name to see its usage. 2022_06_10
Align Illumina SE or PE data with bwa. Produces a sorted, indexed,
duplicate-marked BAM file and various statistics files. Usage: <aln_mode> <in_file> <out_pfx> <assembly> [ paired trim_sz trim_sz2 seq_fmt qual_fmt ]

Required arguments:
  aln_mode  Alignment mode, either global (bwa aln) or local (bwa mem).
  in_file   For single-end alignments, path to input sequence file.
            For paired-end alignments using fastq, path to the the R1
            fastq file which must contain the string 'R1' in its name.
            The corresponding 'R2' must have the same path except for 'R1'.
  out_pfx   Desired prefix of output files in the current directory.
  assembly  One of hg38, hg19, hg38, mm10, mm9, sacCer3, sacCer1, ce11, ce10,
            danRer7, hs_mirbase, mm_mirbase, or reference index prefix.
Optional arguments:
  paired    0 = single end alignment (default); 1 = paired end.
  trim_sz   Size to trim reads to. Default 0 (no trimming)
  trim_sz2  Size to trim R2 reads to for paired end alignments.
            Defaults to trim_sz
  seq_fmt   Format of sequence file (fastq, bam or scarf). Default is
            fastq if the input file has a '.fastq' extension; scarf
            if it has a '.sequence.txt' extension.
  qual_type Type of read quality scores (sanger, illumina or solexa).
            Default is sanger for fastq, illumina for scarf.
Environment variables:
  show_only  1 = only show what would be done (default not set)
  aln_args   other bowtie2 options (e.g. '-T 20' for mem, '-l 20' for aln)
  no_markdup 1 = don't mark duplicates (default 0, mark duplicates)
  run_fastqc 1 = run fastqc (default 0, don't run). Note that output
             will be in the directory containing the fastq files.
  keep       1 = keep unsorted BAM (default 0, don't keep)
  bwa_bin    BWA binary to use. Default bwa 0.7.x. Note that bwa 0.6.2
             or earlier should be used for scarf and other short reads.

Examples: local  ABC_L001_R1.fastq.gz my_abc hg38 1 global ABC_L001_R1.fastq.gz my_abc hg38 1 50 global sequence.txt old sacCer3 0 '' '' scarf solexa

There are lots of bells and whistles in the arguments, but the most important are the first few:

  1. aln_mode – whether to perform a global or local alignment (the 1st argument must be one of those words)
    • global mode uses the bwa aln workflow as we did above
    • local mode uses the bwa mem command
  2. in_file – full or relative path to the FASTQ file (just the R1 fastq if paired end). Can be compressed (.gz)
  3. out_pfxprefix for all the output files produced by the script. Should relate back to what the data is.
  4. assembly – genome assembly to use.
    • there are pre-built indexes for some common eukaryotes (hg38, hg19, mm10, mm9, danRer7, sacCer3) that you can use
    • or provide a full path for a bwa reference index you have built somewhere
  5. paired flag 0 means single end (the default); 1 means paired end
  6. trim_sz – if you want the FASTQ hard trimmed down to a specific length before alignment, supply that number here

We're going to run this script and a similar Bowtie2 alignment script, on the yeast data using the TACC batch system. In a new directory, copy over the commands and submit the batch job. We ask for 2 hours (-t 02:00:00) with 4 tasks/node (-w 4); since we have 4 commands, this will run on 1 compute node.

Run multiple alignments using the TACC batch system
# Make sure you're not in an idev session by looking at the hostname
# If the hostname looks like "", exit the idev session

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

# Make a new alignment directory for running these scripts
mkdir -p $SCRATCH/core_ngs/alignment/bwa_script
cd $SCRATCH/core_ngs/alignment/bwa_script
ln -sf ../fastq

# Copy the alignment commands file and submit the batch job
cd $SCRATCH/core_ngs/alignment/bwa_script 
cp $CORENGS/tacc/aln_script.cmds .

# Use -a TRA23004 below if OTH21164 hasn't been working for you... -j aln_script.cmds -n aln_script -t 01:00:00 -w 4 -a OTH21164 -q normal
sbatch --reservation=CoreNGS-Thu aln_script.slurm 

# or -j aln_script.cmds -n aln_script -t 01:00:00 -w 4 -a OTH21164 -q development
sbatch aln_script.slurm  

showq -u

While we're waiting for the job to complete, lets look at the aln_script.cmds file.

Commands to run multiple alignment scripts
/work/projects/BioITeam/common/script/     global ./fastq/ bwa_global sacCer3 1 50
/work/projects/BioITeam/common/script/     local  ./fastq/ bwa_local  sacCer3 1
/work/projects/BioITeam/common/script/ global ./fastq/ bt2_global sacCer3 1 50
/work/projects/BioITeam/common/script/ local  ./fastq/ bt2_local  sacCer3 1


  • The 1st command performs a paired-end BWA global alignment (similar to above), but asks that the 100 bp reads be trimmed to 50 first.
    • we refer to the pre-built index for yeast by name: sacCer3
      • this index is located in the /work/projects/BioITeam/ref_genome/bwa/bwtsw/sacCer3/ directory
    • we provide the name of the R1 FASTQ file
      • because we request a PE alignment (the 1 argument) the script will look for a similarly-named R2 file.
    • all output files associated with this command will be named with the prefix bwa_global.
  • The 2nd command performs a paired-end BWA local alignment.
    • all output files associated with this command will be named with the prefix bwa_local.
    • no trimming is requested because the local alignment should ignore 5' and 3' bases that don't match the reference genome
  • The 3rd command performs a paired-end Bowtie2 global alignment.
    • the Bowtie2 alignment script has the same first arguments as the BWA alignment script.
    • all output files associated with this command will be named with the prefix bt2_global.
    • again, we specify that reads should first be trimmed to 50 bp.
  • The 4th command performs a paired-end Bowtie2 local alignment.
    • all output files associated with this command will be named with the prefix bt2_local.
    • again, no trimming is requested for the local alignment.

Output files

This alignment pipeline script performs the following steps:

  • Hard trims FASTQ, if optionally specified (fastx_trimmer)
  • Performs the global or local alignment (here, a PE alignment)
    • BWA globalbwa aln the R1 and R2 separately, then bwa sampe to produce a SAM file
    • BWA local: call bwa mem with both R1 and R2 to produce a SAM file
    • Bowtie2 global: call bowtie2 in its default global (end-to-end) mode on both R1 and R2 to produce a SAM file
    • Bowtie2 local: call bowtie2 --local with both R1 and R2 to produce a SAM file
  • Converts SAM to BAM (samtools view)
  • Sorts the BAM (samtools sort)
  • Marks duplicates (Picard MarkDuplicates)
  • Indexes the sorted, duplicate-marked BAM (samtools index)
  • Gathers statistics (samtools idxstats, samtools flagstat, plus a custom statistics script of Anna's)
  • Removes intermediate files

There are a number of output files, with the most important being those desribed below.

  1. <prefix>.align.log – Log file of the entire alignment process.
    • check the tail of this file to make sure the alignment was successful
  2. <prefix>.sort.dup.bam – Sorted, duplicate-marked alignment file.
  3. <prefix>.sort.dup.bam.bai – Index for the sorted, duplicate-marked alignment file
  4. <prefix>.flagstat.txt samtools flagstat output
  5. <prefix>.idxstats.txt samtools idxstats output
  6. <prefix>.samstats.txt – Summary alignment statistics from Anna's stats script
  7. <prefix>.iszinfo.txt – Insert size statistics (for paired-end alignments) from Anna's stats script

Verifying alignment success

The alignment log will have a  "I ran successfully" message at the end if all went well, and if there was an error, the important information should also be at the end of the log file. So you can use tail to check the status of an alignment. For example:

Checking the alignment log file
tail bwa_global.align.log

This will show something like:

..Done bamstats - 2022-06-10 12:59:05
.. samstats file 'bwa_global.samstats.txt' exists and is not empty - 2022-06-10 12:59:05
## Cleaning up files (keep 0) - 2022-06-10 12:59:05
ckRes 0 cleanup
## All bwa alignment tasks completed successfully! - 2022-06-10 12:59:06

Notice that success message:  "All bwa alignment tasks completed successfully!". It should only appear once in any successful alignment log.

When multiple alignment commands are run in parallel it is important to check them all, and you can use grep looking for part of the unique success message to do this. For example:

Count the number of successful alignments
grep 'completed successfully!' *align.log | wc -l

If this command returns 4 (the number of alignment tasks we performed), all went well, and we're done.

But what if something went wrong? How can we tell which alignment task was not successful? You could tail the log files one by one to see which one(s) don't have the message, but you can also use a special grep option to do this work.

Check for failed alignment tasks
grep -L 'completed successfully' *.align.log

The -L option tells grep to only print the filenames that don't contain the pattern. Perfect! To see happens in the case of failure, try it on a file that doesn't contain that message:

grep -L 'completed successfully' aln_script.cmds

Checking alignment statistics

The <prefix>.samstats.txt statistics files produced by the alignment pipeline has a lot of good information in one place. If you look at bwa_global.samstats.txt you'll see something like this:

<prefix>.samstats.txt output
             Aligner:       bwa
     Total sequences:   1184360
        Total mapped:    539079 (45.5 %)
      Total unmapped:    645281 (54.5 %)
             Primary:    539079 (100.0 %)
          Duplicates:    249655 (46.3 %)
          Fwd strand:    267978 (49.7 %)
          Rev strand:    271101 (50.3 %)
          Unique hit:    503629 (93.4 %)
           Multi hit:     35450 (6.6 %)
           Soft clip:
           All match:    531746 (98.6 %)
              Indels:      7333 (1.4 %)
       Total PE seqs:   1184360
      PE seqs mapped:    539079 (45.5 %)
        Num PE pairs:    592180
   F5 1st end mapped:    372121 (62.8 %)
   F3 2nd end mapped:    166958 (28.2 %)
     PE pairs mapped:     80975 (13.7 %)
     PE proper pairs:     16817 (2.8 %)

Since this was a paired end alignment there is paired-end specific information reported. Note that this Yeast dataset was of poor quality, especially the R2 reads. You can see this by the relatively low R1 alignment rate (~63%) and the really low R2 alignment rate (~28%).

You can also view statistics on insert sizes for properly paired reads in the bwa_global.iszinfo.txt file. This tells you the average (mean) insert size, standard deviation, mode (most common value), and fivenum values (minimum, 1st quartile, median, 3rd quartile, maximum).

<prefix>.iszinfo.txt output
  Insert size stats for: bwa_global
        Number of pairs: 16807 (proper)
 Number of insert sizes: 406
        Mean [-/+ 1 SD]: 296 [176 416]  (sd 120)
         Mode [Fivenum]: 228  [51 224 232 241 500]

A quick way to check alignment stats if you have run multiple alignments is again to use grep. For example:

Review multiple alignment rates
grep 'Total mapped' *samstats.txt

will produce output like this:

bt2_global.samstats.txt:        Total mapped:    602893 (50.9 %)
bt2_local.samstats.txt:        Total mapped:    788069 (66.5 %)
bwa_global.samstats.txt:        Total mapped:    539079 (45.5 %)
bwa_local.samstats.txt:        Total mapped:   1008000 (76.5 %

Exercise: How would you list the median insert size for all the alignments?


That information is in the *.iszinfo.txt files, on the line labeled Mode.

The median value is th 3rd value in the 5 fivnum values; it is the 7th whitespace-separated field on the Mode line.


Use grep to isolate the Mode line, and awk to isolate the median value field:

grep 'Mode' *.iszinfo.txt | awk '{print $1,"Median insert size:",$7}'

TACC batch system considerations

The great thing about pipeline scripts like this is that you can perform alignments on many datasets in parallel at TACC, and they are written to take advantage of having multiple cores on TACC nodes where possible.

On the ls6 the pipeline scripts are designed to run best with no more than 4 tasks per node. Although each ls6 node has 128 physical cores per node, the alignment workflow is heavily I/O bound overall, and we don't want to overload the file system.

Always specify wayness 4 for alignment pipeline scripts

These alignment scripts should always be run with a wayness of 4 (-w 4) in the ls6 batch system, meaning at most 4 commands per node.

Exercise #4: Bowtie2 alignment - Vibrio cholerae RNA-seq

While we have focused on aligning eukaryotic data, the same tools can be used with prokaryotic data. The major differences are less about the underlying data and much more about the external/public databases that store and distribute reference data. If we want to study a prokaryote, the reference data is usually downloaded from a resource like GenBank.  

In this exercise, we will use some RNA-seq data from Vibrio cholerae, published on GEO here, and align it to a reference genome.

Overview of Vibrio cholerae alignment workflow with Bowtie2

Alignment of this prokaryotic data follows the workflow below. Here we will concentrate on steps 1 and 2.

  1. Prepare the vibCho reference index for bowtie2 from GenBank records
  2. Align reads using bowtie2, producing a SAM file
  3. Convert the SAM file to a BAM file (samtools view) 
  4. Sort the BAM file by genomic location (samtools sort)
  5. Index the BAM file (samtools index)
  6. Gather simple alignment statistics (samtools flagstat and samtools idxstats)

Obtaining the GenBank records

First prepare a directory for the vibCho fasta, and change to it:

mkdir -p $SCRATCH/core_ngs/references/fasta
cd $SCRATCH/core_ngs/references/fasta

V. cholerae has two chromosomes. We download each separately.

  1. Navigate to
    • click on the Send to down arrow (top right of page)
      • select Complete Record
      • select File as Destination, and Format FASTA
      • click Create File
    • in the Opening File dialog, select Save File then OK
      • Save the file on your local computer as NC_012582.fa
  2. Back on the main page
    • click on the Send to down arrow (top right of page)
      • select Complete Record
      • select File as Destination, and Format GFF3
      • click Create File
    • in the Opening File dialog, select Save File then OK
      • Save the file on your local computer as NC_012582.gff3
  3. Repeat steps 1 and 2 for the 2nd chromosome
  4. Transfer the files from your local computer to TACC
    • to the ~/scratch/core_ngs/references/vibCho directory created above

 Get the 4 downloaded files here
mkdir -p $SCRATCH/core_ngs/references/fasta
cd $SCRATCH/core_ngs/references/fasta
cp $CORENGS/references/fasta/NC_* .

Once you have the 4 files locally in your $SCRATCH/core_ngs/references/vibCho directory, combine them using cat:

cd $SCRATCH/core_ngs/references/fasta
cat NC_01258[23].fa   > vibCho.O395.fa
cat NC_01258[23].gff3 > vibCho.O395.gff3

# verify there are 2 contigs in vibCho.O395.fa
grep -P '^>' vibCho.O395.fa

Now we have a reference sequence file that we can use with the bowtie2 reference builder, and ultimately align sequence data against.

Introducing bowtie2

First make sure you're in an idev session:

Start an idev session
idev -m 120 -A OTH21164 -N 1 -r CoreNGS      # or -A TRA23004 
# or
idev -m 90 -A OTH21164 -N 1 -p development   # or -A TRA23004 

Go ahead and load the bowtie2 module so we can examine some help pages and options.

module load biocontainers
module load bowtie2

 Now that it's loaded, check out the options. There are a lot of them! In fact for the full range of options and their meaning, Google "Bowtie2 manual" and bring up that page ( The Table of Contents is several pages long! Ouch!

This is the key to using bowtie2 - it allows you to control almost everything about its behavior, which make it the go-to aligner for specialized alignment tasks (e.g. aligning miRNA or other small reads). But it also makes it is much more challenging to use than bwa – and it's easier to screw things up too!

Building the bowtie2 vibCho index

Before the alignment, of course, we've got to build a bowtie2- specific index using bowtie2-build. Go ahead and check out its options. Unlike for the aligner itself, we only need to worry about a few things here:

  • reference_in file is just the vibCho.O395.fa FASTA we built from GenBank records
  • bt2_index_base is the prefix of all the bowtie2-build output file

Here, to build the reference index for alignment, we only need the FASTA file. (This is not always true - extensively spliced transcriptomes requires splice junction annotations to align RNA-seq data properly.)

First create a directory specifically for the bowtie2 index, then build the index using bowtie-build.

 Setup (if needed)
mkdir -p $SCRATCH/core_ngs/references/fasta
cd $SCRATCH/core_ngs/references/fasta
cp $CORENGS/references/fasta/vibCho* .

Prepare Bowtie2 index files for vibCho
mkdir -p $SCRATCH/core_ngs/references/bt2/vibCho
cd $SCRATCH/core_ngs/references/bt2/vibCho

# Symlink to the fasta file you created using relative path syntax
ln -sf ../../fasta/vibCho.O395.fa

bowtie2-build vibCho.O395.fa vibCho.O395

This should also go pretty fast. You can see the resulting files using ls like before.

Performing the bowtie2 alignment

Make sure you're in an idev session with the bowtie2 BioContainers module loaded:

idev -m 120 -A OTH21164  -N 1 -r CoreNGS    # or -A TRA23004
# or
idev -m 90 -A OTH21164 -N 1 -p development  # or -A TRA23004

module load biocontainers
module load bowtie2

We'll set up a new directory to perform the V. cholerae data alignment. But first make sure you have the FASTQ file to align and the vibCho bowtie2 index.

# Get a pre-built vibCho index if you didn't already build one
mkdir -p $SCRATCH/core_ngs/references/bt2/vibCho
cp $CORENGS/references/bt2/vibCho/*.*  $SCRATCH/core_ngs/references/bt2/vibCho/

# Get the FASTQ to align
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*fastq.gz  $SCRATCH/core_ngs/alignment/fastq/

Now set up a directory to do this alignment, with symbolic links to the bowtie2 index directory and the directory containing the FASTQ to align:

mkdir -p $SCRATCH/core_ngs/alignment/vibCho
cd $SCRATCH/core_ngs/alignment/vibCho
ln -sf ../../references/bt2/vibCho
ln -sf ../../alignment/fastq fq

We'll be aligning the V. cholerae reads now in ./fq/cholera_rnaseq.fastq.gz (how many sequences does it contain?)

Note that here the data is from standard mRNA sequencing, meaning that the DNA fragments are typically longer than the reads. There is likely to be very little contamination that would require using a local rather than global alignment, or many other pre-processing steps (e.g. adapter trimming). Thus, we will run bowtie2 with default parameters, omitting options other than the input, output, and reference index. This performs a global alignment.

As you can tell from looking at the bowtie2 help message, the general syntax looks like this:

bowtie2 [options]* -x <bt2-idx> {-1 <m1> -2 <m2> | -U <r>} [-S <sam>]

So execute this bowtie2 global, single-end alignment command:

 Setup (if needed)
mkdir -p $SCRATCH/core_ngs/references/fasta
cp $CORENGS/references/fasta/vibCho*  $SCRATCH/core_ngs/references/fasta/

mkdir -p $SCRATCH/core_ngs/references/bt2/vibCho
cp $CORENGS/references/bt2/vibCho/*.*  $SCRATCH/core_ngs/references/bt2/vibCho/

mkdir -p $SCRATCH/core_ngs/alignment/vibCho
cd $SCRATCH/core_ngs/alignment/vibCho
ln -sf ../../references/bt2/vibCho
ln -sf ../../alignment/fastq fqmkdir -p $SCRATCH/core_ngs/alignment/vibCho
cd $SCRATCH/core_ngs/alignment/vibCho
bowtie2 -x vibCho/vibCho.O395 -U fq/cholera_rnaseq.fastq.gz \
  -S cholera_rnaseq.sam 2>&1 | tee aln_global.log


  • -x  vibCho/vibCho.O395.fa – prefix path of index files
  • -U fq/cholera_rnaseq.fastq.gz – FASTQ file for single-end (Unpaired) alignment
  • -S cholera_rnaseq.sam – tells bowtie2 to report alignments in SAM format to the specified file
  • 2>&1 redirects standard error to standard output
    • while the alignment data is being written to the cholera_rnaseq.sam file, bowtie2 will report its progress to standard error.
  • | tee aln.log takes the bowtie2 progress output and pipes it to the tee program
    • tee takes its standard input and writes it to the specified file and also to standard output
    • that way, you can see the progress output now, but also save it to review later (or supply to MultiQC)

Since the FASTQ file is not large, this should not take too long, and you will see progress output like this:

89006 reads; of these:
  89006 (100.00%) were unpaired; of these:
    5902 (6.63%) aligned 0 times
    51483 (57.84%) aligned exactly 1 time
    31621 (35.53%) aligned >1 times
93.37% overall alignment rate

When the job is complete you should have a cholera_rnaseq.sam file that you can examine using whatever commands you like.  Remember, to further process it downstream, you should create a sorted, indexed BAM file from this SAM output.

Exercise: Repeat the alignment performing a local alignment, and write the output in BAM format. How do the alignment statistics compare?



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

Reports these alignment statistics:

89006 reads; of these:
  89006 (100.00%) were unpaired; of these:
    13359 (15.01%) aligned 0 times
    46173 (51.88%) aligned exactly 1 time
    29474 (33.11%) aligned >1 times
84.99% overall alignment rate

Interestingly, the local alignment rate here is lower than we saw with the global alignment. Usually local alignments have higher alignment rates than corresponding global ones.

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 provides the simplicity of using bwa without the complexities of local alignment
  • 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 that includes numerous 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 STAR, hisat2 or tophat have more complex algorithms (as shown below) – and take a lot more time!

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 structure 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).

BWA MEM splits junction-spanning reads into two alignment records

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 /work/projects/BioITeam/ref_genome area.

Run multiple alignments using the TACC batch system
# Make sure you're in an idev session
idev -m 120 -N 1 -A OTH21164 -r CoreNGS      # or -A TRA23004
# or
idev -m 90 -N 1 -A OTH21164 -p development   # or -A TRA23004

# 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 core_ngs/alignment/bwamem
cd core_ngs/alignment/bwamem
ln -sf ../fastq
ln -sf /work/projects/BioITeam/ref_genome/bwa/bwtsw/hg38

Now take a look at bwa mem usage (type bwa mem with no arguments, or bwa mem 2>&1 | more). The most important parameters are the following:

-kControls the minimum seed length (default = 19)
-wControls the "gap bandwidth", or the length of a maximum gap. This is particularly relevant for MEM, since it can determine whether a read is split into two separate alignments or is reported as one long alignment with a long gap in the middle (default = 100)
-MFor split reads, mark the shorter read as secondary
-rControls how long an alignment must be relative to its seed before it is re-seeded to try to find a best-fit local match (default = 1.5, e.g. the value of -k multiplied by 1.5)
-cControls how many matches a MEM must have in the genome before it is discarded (default = 10000)
-tControls the number of threads to use

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
    • samtools sort takes the binary output from samtools view and writes a sorted 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.


Use the zcat | wc -l  | awk idiom to count FASTQ reads.

Use samtools flagstat to report alignment statistics.


Count the FASTQ file reads:

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

The file has 100,000 reads.

Generate alignment statistics from the sorted BAM file:

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

Output will look like this:

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)

There were 133,570 alignment records reported for the 100,000 input reads.

Because bwa mem can split reads and report two alignment records for the same read, there are 33,570 secondary reads reported here.

Be aware that some downstream tools (for example the Picard suite, often used before SNP calling) do not like it when a read name appears more than once in the SAM file. Such reads can be filtered, but only if they can be identified as secondary by specifying the bwa mem -M option as we did above. This option reports the longest alignment normally but marks additional alignments for the read as secondary (the 0x100 BAM flag). This designation also allows you to easily filter out the secondary reads with samtools view -F 0x104 if desired.