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https://github.com/brentp/genoiser

use the noise
https://github.com/brentp/genoiser

bioinformatics genomics high-throughput-sequencing nim nim-lang

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use the noise

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README 

        
#### genoiser: the noise is the signal



given a lot of alignment files, `genoiser` helps to find the areas of the genome that have noise signals

that result in bad variant (small, SV, ME) calls. It finds per-sample noise and then aggregates across

samples and reports the number of samples at each base in the genome that had a given noise signal. 

These regions can be used as black-list regions instead of or in addition to LCRs.



[mosdepth](https://github.com/brentp/mosdepth) uses chromosome-sized arrays of

int32's to track sequencing depth. This is [fast and flexible](https://brentp.github.io/post/arrays/).



given `mosdepth` as a special-case for *depth*, `genoiser` is a general case for user-defined functions.

`mosdepth` could be implemented with `genoiser`.



An added benefit is the reduction of memory; `mosdepth` allocates an int32 array the size of each

chromosome--meaning about 1GB of memory for chromosome 1. `genoiser` can use smaller-sized chunks to

tile across each chromosome. This is important because it uses 1 array for each user-defined function.

It defaults to 8 megabase chunks as that is the smallest size with no noticeable effect on performance

in our tests. Chunk sizes down to 100KB have some, but minor effect on performance.



The idea is of `genoiser` is that it will handle all accounting, a user

simply defines a [nim](https://nim-lang.org) function that takes an alignment and then

indicates which genomic positions to increment. For example, to calculate depth, this user

function would increment from start to end:



```Nim

proc depthfun*(aln:Record, posns:var seq[mrange]) =

  ## depthfun is an example of a `fun` that can be sent to `genoiser`.

  ## it increments from aln.start to aln.stop of passing reads.

  var f = aln.flag

  if f.unmapped or f.secondary or f.qcfail or f.dup: return

  posns.add((aln.start, aln.stop, 1))

```



The `posns` value is sent to the function by `genoiser` and the user-defined function

can add to it as many elements as desired. In this case it increments from `aln.start`

to `aln.stop` by `1`. It can inrement by any integer value.



The user could also choose to increment any soft or hard-clip location:



```Nim

proc softfun*(aln:Record, posns:var seq[mrange]) =

  ## softfun an example of a `fun` that can be sent to `genoiser`.

  ## it sets positions where there are soft-clips

  var f = aln.flag

  if f.unmapped or f.secondary or f.supplementary or f.qcfail or f.dup: return

  var cig = aln.cigar

  if cig.len == 1: return

  var pos = aln.start



  for op in cig:

    if op.op == CigarOp.soft_clip or op.op == CigarOp.hard_clip:

      # for this function, we want the exact break-points, not the span of the event,

      # so we increment the position and the one that follows it.

      posns.add((pos, pos+1, 1))

    if op.consumes.reference:

      pos += op.len

```



### Utility



This library provides the machinery. Other command-line tools will use this for more obviously useful things.



## Speed



for maximum speed, compile with `nim c -d:release --passC:-flto --passL:-s --gc:markAndSweep src/genoiser.nim`



## CLI



The command-line interface allows running pre-specified noise filters in 2 steps. The first steps calculates the "noise" in each sample.



```

genoiser per-sample --fasta $reference results/$sample /path/to/$sample.bam # or cram

```



This will use a single thread and it will take about 1 hour for 30X bams and a bit more for crams. It will output 5 files per sample.

at the specificied prefix, in this case, `results/$sample`



After all samples have been run, then the user can `aggregate` the signal across samples. The recommended commands are:



```

chroms="$(seq 1 22) X Y"



########

## soft

########

# count number of samples at each site where more than 1 and less than 15% of reads were soft-clipped.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value / depth) < 0.15 & (value > 1)' results/*.genoiser.{}.soft.bed > soft.{}.bed"



# count number of samples at each site where more than 2 where soft-clipped.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value > 2)' results/*.genoiser.{}.soft.bed > high-soft.{}.bed"



###########

## mq0

###########



# count number of samples at each site where more than 2 reads had MQ0.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value > 2)' results/*.genoiser.{}.mq0.bed > mq0.{}.bed"



############

## weird

############

# count number of samples at each site where more than 1 and less than 15% of reads were weird.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value / depth) < 0.15 & (value > 1)' results/*.genoiser.{}.weird.bed > weird.{}.bed"



echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value > 2)' results/*.genoiser.{}.weird.bed > high-weird.{}.bed"



############

## mismatches

############



# count number of samples at each site where 10 or more reads read with 4 or more mismatches overlapped.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e 'value > 10' results/*.genoiser.{}.mismatches.bed > mismatches.{}.bed"



##################

# interchromosomal

##################



# count number of samples at each site where more than 1 and less than 15% of reads were interchromosomal.

echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value / depth) < 0.15 & (value > 1)' results/*.genoiser.{}.interchromosomal.bed > interchromosomal.{}.bed"



echo $chroms | tr ' ' '\n' \

    | gargs -d -v -p 5 "genoiser aggregate -t 10 -e '(value > 2)' results/*.genoiser.{}.interchromosomal.bed > high-interchromosomal.{}.bed"



```

where `gargs` is available as a static binary from [here](https://github.com/brentp/gargs/releases)



This will output 1 file per chromosome, per metric. The resulting files are the desired output.



Note that this will use `5*10` threads. Adjust this to match your CPU requirements.