Step 1: Filters low quality, low complexity, duplicate, and host reads
OverviewFilters low complexity, low quality, duplicate, and host reads. First step in the PathSeq pipeline.
See PathSeqPipelineSpark for an overview of the PathSeq pipeline.
The tool works by filtering out reads in a series of stages:
- Remove secondary and supplementary alignments
- If the input reads are aligned to a host reference, remove mapped reads with sufficient alignment identity
- Trim adapter sequences
- Mask sequences with excessive A/T or G/C content
- Mask repetitive sequences (see Liebert et al. 2006)
- Hard clip read ends using base qualities
- Remove reads that are too short or were trimmed excessively
- Mask low-quality bases
- Remove reads with too many masked bases
- Remove reads containing one or more host k-mers
- Remove reads with sufficient alignment identity to the host reference
- Remove exact duplicate reads
- BAM containing input reads (either unaligned or aligned to a host reference)
- *Host k-mer file generated using PathSeqBuildKmers
- *Host BWA-MEM index image generated using BwaMemIndexImageCreator
*A standard host reference is available in the GATK Resource Bundle.
- BAM containing high quality, non-host paired reads (paired-end reads with both mates)
- BAM containing high quality, non-host unpaired reads (single-end reads and/or paired-end reads without mates)
This tool can be run without explicitly specifying Spark options. That is to say, the given example command without Spark options will run locally. See Tutorial#10060 for an example of how to set up and run a Spark tool on a cloud Spark cluster.
gatk PathSeqFilterSpark \ --input input_reads.bam \ --paired-output output_reads_paired.bam \ --unpaired-output output_reads_unpaired.bam \ --min-clipped-read-length 60 \ --kmer-file host_kmers.bfi \ --filter-bwa-image host_reference.img \ --filter-metrics metrics.txt \ --bam-partition-size 4000000
Spark cluster on Google Cloud DataProc with 4 16-core / 100GB memory worker nodes:
gatk PathSeqFilterSpark \ --input gs://my-gcs-bucket/input_reads.bam \ --paired-output gs://my-gcs-bucket/output_reads_paired.bam \ --unpaired-output gs://my-gcs-bucket/output_reads_unpaired.bam \ --min-clipped-read-length 60 \ --kmer-file hdfs://my-cluster-m:8020//references/host_kmers.bfi \ --filter-bwa-image /references/host_reference.img \ --filter-metrics gs://my-gcs-bucket/metrics.txt \ --bam-partition-size 4000000 \ -- \ --sparkRunner GCS \ --cluster my_cluster \ --driver-memory 8G \ --executor-memory 32G \ --num-executors 4 \ --executor-cores 15 \ --conf spark.yarn.executor.memoryOverhead=32000
Note that the host BWA image must be copied to the same path on every worker node. The host k-mer file may also be copied to a single path on every worker node or to HDFS.
The input BAM may be unaligned (a uBAM) or aligned to a host reference (e.g. hg38). If it is aligned then --is-host-aligned should be enabled. This will substantially increase performance, as host reads can then be immediately subtracted prior to quality filtering and host alignment.
- Liebert, M. A. et al. (2006). A Fast and Symmetric DUST Implementation to Mask Low-Complexity DNA Sequences. J. Comput. Biol., 13, 1028-1040.
PathSeqFilterSpark specific arguments
This table summarizes the command-line arguments that are specific to this tool. For more details on each argument, see the list further down below the table or click on an argument name to jump directly to that entry in the list.
|Argument name(s)||Default value||Summary|
|||BAM/SAM/CRAM file containing reads|
|Optional Tool Arguments|
||||read one or more arguments files and add them to the command line|
||0||maximum number of bytes to read from a file into each partition of reads. Setting this higher will result in fewer partitions. Note that this will not be equal to the size of the partition in memory. Defaults to 0, which uses the default split size (determined by the Hadoop input format, typically the size of one HDFS block).|
||||spark properties to set on the spark context in the format
||false||If specified, do not check the sequence dictionaries from our inputs for compatibility. Use at your own risk!|
||2||Base quality to assign low-complexity bases|
||20.0||DUST algorithm score threshold|
||64||DUST algorithm window size|
|null||The BWA image file of the host reference. This must be distributed to local disk on each node.|
||19||Minimum seed length for the host BWA alignment.|
||true||Filter duplicate reads|
|null||Log counts of filtered reads to this file|
|20||If the GCS bucket channel errors out, how many times it will attempt to re-initiate the connection|
|false||display the help message|
||1||Host kmer count threshold.|
||30||Host alignment identity score threshold, in bp|
|ALL||Interval merging rule for abutting intervals|
|||One or more genomic intervals over which to operate|
||false||Set if the input BAM is aligned to the host|
|null||Path to host k-mer file generated with PathSeqBuildKmers. K-mer filtering is skipped if this is not specified.|
||1||Maximum number of mismatches for adapter trimming|
||2||Max allowable number of masked bases per read|
||12||Minimum length of adapter sequence to trim|
||15||Bases below this call quality will be masked with 'N'|
||31||Minimum length of reads after quality trimming|
||0||For tools that shuffle data or write an output, sets the number of reducers. Defaults to 0, which gives one partition per 10MB of input.|
||null||when writing a bam, in single sharded mode this directory to write the temporary intermediate output shards, if not specified .parts/ will be used|
|null||Output BAM containing only paired reads|
||null||Name of the program running|
||15||Quality score trimmer threshold|
||false||For tools that write an output, write the output in multiple pieces (shards)|
||false||Skip low-quality and low-complexity read filtering|
||local[*]||URL of the Spark Master to submit jobs to when using the Spark pipeline runner.|
|null||Output BAM containing only unpaired reads|
||false||display the version number for this tool|
|Optional Common Arguments|
|||Read filters to be disabled before analysis|
||false||Disable all tool default read filters (WARNING: many tools will not function correctly without their default read filters on)|
|||One or more genomic intervals to exclude from processing|
||null||A configuration file to use with the GATK.|
|0||Amount of padding (in bp) to add to each interval you are excluding.|
|0||Amount of padding (in bp) to add to each interval you are including.|
|UNION||Set merging approach to use for combining interval inputs|
||false||Whether to suppress job-summary info on System.err.|
|||Read filters to be applied before analysis|
||||Indices to use for the read inputs. If specified, an index must be provided for every read input and in the same order as the read inputs. If this argument is not specified, the path to the index for each input will be inferred automatically.|
|SILENT||Validation stringency for all SAM/BAM/CRAM/SRA files read by this program. The default stringency value SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.|
|false||Whether to use the JdkDeflater (as opposed to IntelDeflater)|
|false||Whether to use the JdkInflater (as opposed to IntelInflater)|
||INFO||Control verbosity of logging.|
||200000||Estimated reads per partition after quality, kmer, and BWA filtering|
||false||display hidden arguments|
||false||Skip pre-BWA repartition. Set to true for inputs with a high proportion of microbial reads that are not host coordinate-sorted.|
Arguments in this list are specific to this tool. Keep in mind that other arguments are available that are shared with other tools (e.g. command-line GATK arguments); see Inherited arguments above.
--arguments_file / NA
read one or more arguments files and add them to the command line
--bam-partition-size / NA
maximum number of bytes to read from a file into each partition of reads. Setting this higher will result in fewer partitions. Note that this will not be equal to the size of the partition in memory. Defaults to 0, which uses the default split size (determined by the Hadoop input format, typically the size of one HDFS block).
long 0 [ [ -∞ ∞ ] ]
--conf / -conf
spark properties to set on the spark context in the format
--disable-read-filter / -DF
Read filters to be disabled before analysis
--disable-sequence-dictionary-validation / -disable-sequence-dictionary-validation
--disable-tool-default-read-filters / -disable-tool-default-read-filters
--dust-mask-quality / -dust-mask-quality
Base quality to assign low-complexity bases
int 2 [ [ -∞ ∞ ] ]
--dust-t / -dust-t
DUST algorithm score threshold
Controls the stringency of low-complexity filtering.
double 20.0 [ [ -∞ ∞ ] ]
--dust-window / -dust-window
DUST algorithm window size
int 64 [ [ -∞ ∞ ] ]
--exclude-intervals / -XL
One or more genomic intervals to exclude from processing
Use this argument to exclude certain parts of the genome from the analysis (like -L, but the opposite). This argument can be specified multiple times. You can use samtools-style intervals either explicitly on the command line (e.g. -XL 1 or -XL 1:100-200) or by loading in a file containing a list of intervals (e.g. -XL myFile.intervals).
--filter-bwa-image / -FI
The BWA image file of the host reference. This must be distributed to local disk on each node.
This file should be generated using BwaMemIndexImageCreator.
--filter-bwa-seed-length / -filter-bwa-seed-length
Minimum seed length for the host BWA alignment.
Controls the sensitivity of BWA alignment to the host reference. Shorter seed lengths will enhance detection of host reads during the subtraction phase but will also increase run time.
int 19 [ [ 1 [ 11 ∞ ] ]
--filter-duplicates / -filter-duplicates
Filter duplicate reads
If true, then for any two reads with identical sequences (or identical to the other's reverse complement), one will be filtered.
--filter-metrics / -FM
Log counts of filtered reads to this file
If specified, records the number of reads remaining after each of the following steps:
- Pre-aligned host read filtering
- Low-quality and low-complexity sequence filtering
- Host read subtraction
- Read deduplication
It also provides the following:
- Number of low-quality and low-complexity reads removed
- Number of host reads removed
- Number of duplicate reads removed
- Final number of reads
- Final number of paired reads
- Final number of unpaired reads
Note that using this option may substantially increase runtime.
--filter-reads-per-partition / -filter-reads-per-partition
Estimated reads per partition after quality, kmer, and BWA filtering
This is a parameter for fine-tuning memory performance. Lower values may result in less memory usage but possibly at the expense of greater computation time.
int 200000 [ [ 1 ∞ ] ]
--gatk-config-file / NA
A configuration file to use with the GATK.
--gcs-max-retries / -gcs-retries
If the GCS bucket channel errors out, how many times it will attempt to re-initiate the connection
int 20 [ [ -∞ ∞ ] ]
--help / -h
display the help message
--host-kmer-thresh / -host-kmer-thresh
Host kmer count threshold.
Controls the stringency of read filtering based on host k-mer matching. Reads with at least this many matching k-mers in the host reference will be filtered.
int 1 [ [ 1 ∞ ] ]
--host-min-identity / -host-min-identity
Host alignment identity score threshold, in bp
Controls the stringency of read filtering based on alignment to the host reference. The identity score is defined as the number of matching bases less the number of deletions in the alignment.
int 30 [ [ 1 ∞ ] ]
--input / -I
BAM/SAM/CRAM file containing reads
R List[String] 
--interval-exclusion-padding / -ixp
Amount of padding (in bp) to add to each interval you are excluding.
Use this to add padding to the intervals specified using -XL. For example, '-XL 1:100' with a padding value of 20 would turn into '-XL 1:80-120'. This is typically used to add padding around targets when analyzing exomes.
int 0 [ [ -∞ ∞ ] ]
--interval-merging-rule / -imr
Interval merging rule for abutting intervals
By default, the program merges abutting intervals (i.e. intervals that are directly side-by-side but do not actually overlap) into a single continuous interval. However you can change this behavior if you want them to be treated as separate intervals instead.
The --interval-merging-rule argument is an enumerated type (IntervalMergingRule), which can have one of the following values:
--interval-padding / -ip
Amount of padding (in bp) to add to each interval you are including.
Use this to add padding to the intervals specified using -L. For example, '-L 1:100' with a padding value of 20 would turn into '-L 1:80-120'. This is typically used to add padding around targets when analyzing exomes.
int 0 [ [ -∞ ∞ ] ]
--interval-set-rule / -isr
Set merging approach to use for combining interval inputs
By default, the program will take the UNION of all intervals specified using -L and/or -XL. However, you can change this setting for -L, for example if you want to take the INTERSECTION of the sets instead. E.g. to perform the analysis only on chromosome 1 exomes, you could specify -L exomes.intervals -L 1 --interval-set-rule INTERSECTION. However, it is not possible to modify the merging approach for intervals passed using -XL (they will always be merged using UNION). Note that if you specify both -L and -XL, the -XL interval set will be subtracted from the -L interval set.
The --interval-set-rule argument is an enumerated type (IntervalSetRule), which can have one of the following values:
- Take the union of all intervals
- Take the intersection of intervals (the subset that overlaps all intervals specified)
--intervals / -L
One or more genomic intervals over which to operate
--is-host-aligned / -is-host-aligned
Set if the input BAM is aligned to the host
PathSeq will rapidly filter the reads if they are aligned to a host reference, thus reducing run time.
--kmer-file / -K
Path to host k-mer file generated with PathSeqBuildKmers. K-mer filtering is skipped if this is not specified.
--max-adapter-mismatches / -max-adapter-mismatches
Maximum number of mismatches for adapter trimming
int 1 [ [ 0 ∞ ] ]
--max-masked-bases / -max-masked-bases
Max allowable number of masked bases per read
This is the threshold for filtering reads based on the number of 'N' values present in the sequence. Note that the low-complexity DUST filter and quality filter mask using 'N' bases. Therefore, this parameter is the threshold for the sum of:
- The number of N's in the original input read
- The number of low-quality base calls
- The number of low-complexity bases
int 2 [ [ 0 ∞ ] ]
--min-adapter-length / -min-adapter-length
Minimum length of adapter sequence to trim
Adapter trimming will require a match of at least this length to a known adapter.
int 12 [ [ 1 ∞ ] ]
--min-base-quality / -min-base-quality
Bases below this call quality will be masked with 'N'
int 15 [ [ 1 ∞ ] ]
--min-clipped-read-length / -min-clipped-read-length
Minimum length of reads after quality trimming
Reads are trimmed based on base call quality and low-complexity content. Decreasing the value will enhance pathogen detection (higher sensitivity) but also result in undesired false positives and ambiguous microbe alignments (lower specificity).
int 31 [ [ 1 [ 31 ∞ ] ]
--num-reducers / NA
For tools that shuffle data or write an output, sets the number of reducers. Defaults to 0, which gives one partition per 10MB of input.
int 0 [ [ -∞ ∞ ] ]
when writing a bam, in single sharded mode this directory to write the temporary intermediate output shards, if not specified .parts/ will be used
Exclusion: This argument cannot be used at the same time as
--paired-output / -PO
Output BAM containing only paired reads
--program-name / NA
Name of the program running
--quality-threshold / -quality-threshold
Quality score trimmer threshold
Controls the stingency of base call quality-based read trimming. Higher values result in more trimming.
int 15 [ [ 1 ∞ ] ]
--QUIET / NA
Whether to suppress job-summary info on System.err.
--read-filter / -RF
Read filters to be applied before analysis
--read-index / -read-index
The --read-validation-stringency argument is an enumerated type (ValidationStringency), which can have one of the following values:
--reference / -R
--sharded-output / NA
For tools that write an output, write the output in multiple pieces (shards)
Exclusion: This argument cannot be used at the same time as
--showHidden / -showHidden
display hidden arguments
--skip-pre-bwa-repartition / -skip-pre-bwa-repartition
Skip pre-BWA repartition. Set to true for inputs with a high proportion of microbial reads that are not host coordinate-sorted.
Advanced optimization option that should be used only in the case of inputs with a high proportion of microbial reads that are not host-aligned/coordinate-sorted.
In the filter tool, the input reads are initially divided up into smaller partitions (default size is usually the size of one HDFS block, or ~64MB) that Spark works on in parallel. In samples with a low proportion of microbial reads (e.g. < 1%), the steps leading up to the host BWA alignment will whittle these partitions down to a small fraction of their original size. At that point, the distribution of reads across the partitions may be unbalanced.
For example, say the input is 256MB and Spark splits this into 4 even partitions. It is possible that, after running through the quality filters and host kmer search, there are 5% remaining in partition #1, 8% in partition #2, 2% in partition #3, and 20% in partition #4. Thus there is an imbalance of work across the partitions. To correct this, a "reparitioning" is invoked that distributes the reads evenly. Note this is especially important for host-aligned, coordinate-sorted inputs, in which unmapped reads would be concentrated in the last partitions.
If, however, the proportion of microbial reads is higher, say 30%, then the partitions are generally more balanced (except for in the aforementioned coordinate-sorted case). In this case, the time spent doing the repartitioning is usually greater than the time saved by rebalancing, and this option should be invoked.
--skip-quality-filters / -skip-quality-filters
Skip low-quality and low-complexity read filtering
--spark-master / NA
URL of the Spark Master to submit jobs to when using the Spark pipeline runner.
--TMP_DIR / NA
--unpaired-output / -UO
Output BAM containing only unpaired reads
--use-jdk-deflater / -jdk-deflater
Whether to use the JdkDeflater (as opposed to IntelDeflater)
--use-jdk-inflater / -jdk-inflater
Whether to use the JdkInflater (as opposed to IntelInflater)
--verbosity / -verbosity
Control verbosity of logging.
The --verbosity argument is an enumerated type (LogLevel), which can have one of the following values:
--version / NA
display the version number for this tool
GATK version 126.96.36.199 built at 25-39-2019 01:39:46.