DeepSAP

DeepSAP is a transformer-based workflow designed to enhance splice junction detection in RNA-seq data. By default, DeepSAP utilizes a highly sensitive GPU-accelerated GSNAP TGGA aligner for FASTQ inputs. Alternatively, it can also score pre-aligned BAM files directly — either from GSNAP itself or from any other aligner whose SAM records carry the XA (alternative alignments) tag.

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We evaluated the performance of DeepSAP in our Genome Biology article: DeepSAP: improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring (Berakdar, Wu, Zhu, Samadi, Vats, 2026). In our benchmark, DeepSAP demonstrated strong performance, achieving consistently outstanding results across all evaluated metrics using Baruzzo et al. datasets. For additional resources, including data, detailed analyses, and other supplementary materials accompanying the DeepSAP article, please refer to the DeepSAP GitHub repository.

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Requirements

System Software:

Docker with GPU support

System Hardware:

Sizing below is for a human genome–scale reference (GRCh38). The two pipeline stages run sequentially, so peak GPU memory is the maximum of the alignment-stage and TSJS-stage footprints — not their sum.

CPU & RAM:

CPU: 24 cores recommended (drives GSNAP's pipeline-parallel stages — reader / solver / writer threads — and DeepSAP's TSJS scoring stage).
System RAM: 64 GB minimum.

GPU memory:

Minimum recommended: 40 GB (validated on NVIDIA A100 PCIe 40 GB, H100 PCIe, and RTX A6000 48 GB).
The alignment stage sets the floor; the TSJS stage's GPU memory scales with --batch and --fp16.

Alignment stage (GPU-accelerated GSNAP):

GSNAP transcriptome-guided genome index resident on device: ~24 GB.
--localdb-scratch (Stage-2 localdb GPU scratch buffer): default 12G, tunable.
Default total: ~36 GB. Setting --localdb-scratch=1G brings the alignment-stage footprint down to ~25 GB (fits a 24 GB card with little headroom — closer to 32 GB is comfortable).

TSJS (transformer splice-junction scoring) stage:

GPU memory here is dominated by two parameters:

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--batch: number of candidate splice junctions scored per transformer forward pass. Larger batches significantly improve throughput but require more GPU memory.
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--fp16: half-precision floating-point inference is enabled by default and roughly halves GPU memory versus fp32. Disable with --no-fp16, which approximately doubles the per-batch memory shown below.
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--batch Approximate GPU memory (with --fp16)
64 ~1.2 GB
128 ~1.6 GB
256 ~2.2 GB
2048 (default) ~10.4 GB
8192 ~39.5 GB
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`--batch`	Approximate GPU memory (with `--fp16`)
64	~1.2 GB
128	~1.6 GB
256	~2.2 GB
2048 (default)	~10.4 GB
8192	~39.5 GB

Input Data:

RNA-seq reads in FASTQ format.
Reference file in FASTA format.
Annotation file in GTF format.
Optionally, a path to a GSNAP index.

Usage

This guide demonstrates how to quickly test DeepSAP's functionality using the malaria_short_pe dataset. Follow these steps to set up your environment and run DeepSAP:

Least-privilege container execution

For production runs, mount only the input, index, and output directories that DeepSAP needs. Keep reference data, FASTQ/BAM inputs, and reusable indexes read-only; use a separate writable output directory for results.

Step 1: Prepare Environment and Download Test Data

This step downloads the latest DeepSAP Docker container and all required reference files and test sequencing data.

# Pull the DeepSAP Parabricks Docker image\ndocker pull nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest\n\n# Create writable result directories as the invoking host user\nmkdir -p test/outputdir test/results/prebuilt test/results/rescored\n\n# Download reference genome and annotation files\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n\n# Download downsampled FASTQ sequence reads (10K) from DeepSAP GitHub\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/SRR14793977_10K_1.fastq.gz\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/SRR14793977_10K_2.fastq.gz\n

Step 2: Build a GSNAP Index (`--mode index`)

This command builds a standalone, reusable GSNAP TGGA index from the FASTA + GTF and writes it under <out>/<prefix>/. Useful when you plan to score many samples against the same reference — build the index once, then reuse it in Step 4.

# Build a reusable GSNAP index from the malaria reference\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir:/outputdir:rw\"                                  \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode index                                                                    \\\n    --out /outputdir/                                                               \\\n    --prefix malaria_idx                                                            \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n

Step 3: Run DeepSAP End-to-End (auto-build index, `--mode GSNAP+TSJS`)

This command executes the full DeepSAP pipeline (GSNAP alignment + transformer splice-junction scoring) on the downloaded test dataset using the default --mode GSNAP+TSJS. Since --gsnap_idx is not specified, DeepSAP auto-builds a GSNAP index inline at <out>/gsnap_idx/ before alignment. Pick this path for one-shot runs where you don't need to reuse the index later.

# Run DeepSAP end-to-end (GSNAP index will be auto-generated)\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir:/outputdir:rw\"                                  \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode GSNAP+TSJS                                                               \\\n    --out /outputdir/                                                               \\\n    --prefix test_run_10K                                                           \\\n    --mate_1 /workdir/malaria_short_pe/SRR14793977_10K_1.fastq.gz                   \\\n    --mate_2 /workdir/malaria_short_pe/SRR14793977_10K_2.fastq.gz                   \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n

Step 4: Run DeepSAP with a Pre-existing GSNAP Index (`--mode GSNAP+TSJS` + `--gsnap_idx`)

If you have already generated a GSNAP index (e.g., from Step 2, a previous DeepSAP run, or shared infrastructure), point DeepSAP at it via --gsnap_idx. This skips index construction, runs the same GSNAP alignment pipeline as an auto-indexed run, writes <prefix>_gsnap.bam, and then applies TSJS scoring to that BAM.

# Run DeepSAP using the index built in Step 2\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir/malaria_idx:/gsnap_idx:ro\"                      \\\n    --volume \"$(pwd)/test/results/prebuilt:/outputdir:rw\"                           \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode GSNAP+TSJS                                                               \\\n    --out /outputdir/                                                               \\\n    --prefix test_run_10K                                                           \\\n    --mate_1 /workdir/malaria_short_pe/SRR14793977_10K_1.fastq.gz                   \\\n    --mate_2 /workdir/malaria_short_pe/SRR14793977_10K_2.fastq.gz                   \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\\\n    --gsnap_idx /gsnap_idx/\n

Step 5: Score an Existing BAM with TSJS Only (`--mode GSNAP+TSJS` + `--sam`)

If you already have a GSNAP-aligned BAM (e.g., from a prior GSNAP alignment run, or from any other aligner whose SAM records carry the XA (alternative alignments) tag), pass it via --sam and DeepSAP skips alignment entirely — running transformer splice-junction scoring directly on the BAM. The output is a new BAM with TSJS-derived MAPQ adjustments and junction-scoring metadata.

# Score a pre-aligned BAM (no GSNAP step)\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir/test_run_10K_gsnap.bam:/input.bam:ro\"           \\\n    --volume \"$(pwd)/test/results/rescored:/outputdir:rw\"                           \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode GSNAP+TSJS                                                               \\\n    --out /outputdir/                                                               \\\n    --prefix test_run_10K_rescored                                                  \\\n    --sam /input.bam                                                                \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n

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Note: --sam and --mate_1/--mate_2 are mutually exclusive — DeepSAP either aligns or scores an existing alignment, never both in the same run.
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Pipeline Modes

DeepSAP's --mode flag selects which pipeline mode to run. The default GSNAP+TSJS reproduces the v0.0.x end-to-end behavior; index lets you pre-build a GSNAP index in isolation (useful for sharing a pre-built index across many samples).

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`--mode`	Required inputs	Optional inputs	Outputs
`index`	`--fasta`, `--gtf`	—	GSNAP index at `<out>/<prefix>/`
`GSNAP+TSJS` (default)	`--fasta`, `--gtf`, and either `--mate_1`+`--mate_2` (optionally with `--gsnap_idx`) or `--sam`	model / batching flags, `--score_method`	scored BAM at `<out>/<prefix>.bam` (+ intermediate datasets)

Mode 1: Build a GSNAP index only

docker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir:/outputdir:rw\"                                  \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode index                                                                    \\\n    --out /outputdir/                                                               \\\n    --prefix malaria_idx                                                            \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n

Mode 2: Score an existing BAM with TSJS only

/outputdir/test_run_10K_rescored.bam (TSJS-scored)\n\">

docker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm                \\\n    --user \"$(id -u):$(id -g)\"                                                      \\\n    --cap-drop=ALL --security-opt=no-new-privileges                                 \\\n    --volume \"$(pwd)/test:/workdir:ro\"                                              \\\n    --volume \"$(pwd)/test/outputdir/test_run_10K_gsnap.bam:/input.bam:ro\"           \\\n    --volume \"$(pwd)/test/results/rescored:/outputdir:rw\"                           \\\n    nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest                            \\\n    --mode GSNAP+TSJS                                                               \\\n    --out /outputdir/                                                               \\\n    --prefix test_run_10K_rescored                                                  \\\n    --sam /input.bam                                                                \\\n    --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf           \\\n    --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n# -> /outputdir/test_run_10K_rescored.bam (TSJS-scored)\n

DeepSAP Expected Output

14__-__3220325__3220534\nLongest intron: 2425 CZU00099: 14__+__1639681__1639728 -> 14__+__1642154__1642455\nIntrons length mean: 163.03\nIntrons length median: 141.0\nIntrons length mode: 1\nNumber of multi exons transcripts: 3064 53.13%\nNumber of mono exon transcripts: 2703 46.87%\n\nType of transcripts:\n BioType Count Percentage\n0 protein_coding 5358 92.91\n1 pseudogene 153 2.65\n3 ncRNA 102 1.77\n4 tRNA 79 1.37\n5 rRNA 44 0.76\n7 sRNA 17 0.29\n6 snRNA 10 0.17\n2 nontranslating_CDS 4 0.07\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from GTF\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions in mode=NotStrict and window=150\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from transcript types: All\nNumber of duplicated junctions: 328\nNumber of short junctions (intron): 0\nNumber of short junctions (donor): 0\nNumber of short junctions (acceptor): 0\nNumber of junctions contains N: 0\nNumber of accepted junctions: 8764\nThe First 10 Splicing Signals Types: \nSignal Forward Reverse Percentage\n GTAG 4096 4431 97.30\n AAAA 18 17 0.40\n TATA 12 8 0.23\n GCAG 9 9 0.21\n TTTT 6 9 0.17\n ATAT 4 7 0.13\n GAGA 5 6 0.13\n AGAG 3 6 0.10\n TATT 3 6 0.10\n TAAT 4 5 0.10\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from SAM/BAM file '/outputdir/test_run_10K_gsnap.bam'\n[2025-07-18 12:52:47] [INFO] Sense junctions 518\n[2025-07-18 12:52:47] [INFO] Antisense junctions 551\n[2025-07-18 12:52:47] [INFO] Total number of reads 20479\n[2025-07-18 12:52:47] [INFO] Total number of spliced reads 2233 10.903852727183946%\n[2025-07-18 12:52:47] [LOG] Finished parsing a SAM file, len(found_junctions_table)= 1069\n[2025-07-18 12:52:47] [LOG] Generating splice-junction prediction dataset batch: 1\n[2025-07-18 12:52:47] [LOG] Writting dev.csv file for predicting into '/outputdir/test_run_10K_prediction_batch_1/'\n[2025-07-18 12:52:47] [LOG] dev.csv file contains: 0: 1069, 1: 1069\n[2025-07-18 12:52:47] [LOG] Predicting found splice junctions using DNABERT MS150\n100%|██████████| 67/67 [00:01<00:00, 58.23it/s]\n[2025-07-18 12:52:51] [LOG] Generating genome regions \n[2025-07-18 12:52:51] [LOG] Parsing FASTA file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa'\n[2025-07-18 12:52:53] [LOG] Finished writing BAM successfully into '/outputdir/test_run_10K'\n[2025-07-18 12:52:53] [LOG] Number of SAM records: 20479 \n[2025-07-18 12:52:53] [LOG] Number of reads IDs: 12644 \n[2025-07-18 12:52:53] [LOG] Number of processed reads IDs: 1405 11.11% \n\n[2025-07-18 12:52:54] [LOG] Finished successfully\n\">

[2025-07-18 12:51:27]   [INFO]  Running DeepSAP v0.1.0\n[2025-07-18 12:51:32]   [LOG]   Running GSNAP\n[2025-07-18 12:51:32]   [LOG]   Building GSNAP TGGA index\n[2025-07-18 12:52:44]   [LOG]   Running GSNAP TGGA \n[2025-07-18 12:52:46]   [LOG]   Parsing FASTA file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa'\n[2025-07-18 12:52:46]   [LOG]   Parsing GTF file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf'\n[2025-07-18 12:52:47]   [LOG]   Transcript information: \nNumber of transcripts:             5767\nShortest transcript:               67   EPT00050203058\nLongest transcript:                30863        CAG25094\nTranscripts length mean:           2456.79\nTranscripts length median:         1618\nTranscripts length mode:           71\nShortest intron:                   1    PF3D7_1478200: 14__-__3219919__3220323 -> 14__-__3220325__3220534\nLongest intron:                    2425 CZU00099: 14__+__1639681__1639728 -> 14__+__1642154__1642455\nIntrons length mean:               163.03\nIntrons length median:             141.0\nIntrons length mode:               1\nNumber of multi exons transcripts: 3064 53.13%\nNumber of mono exon transcripts:   2703 46.87%\n\nType of transcripts:\n              BioType  Count  Percentage\n0      protein_coding   5358       92.91\n1          pseudogene    153        2.65\n3               ncRNA    102        1.77\n4                tRNA     79        1.37\n5                rRNA     44        0.76\n7                sRNA     17        0.29\n6               snRNA     10        0.17\n2  nontranslating_CDS      4        0.07\n[2025-07-18 12:52:47]   [LOG]   Collecting splice junctions from GTF\n[2025-07-18 12:52:47]   [LOG]   Collecting splice junctions in mode=NotStrict and window=150\n[2025-07-18 12:52:47]   [LOG]   Collecting splice junctions from transcript types: All\nNumber of duplicated junctions:        328\nNumber of short junctions (intron):    0\nNumber of short junctions (donor):     0\nNumber of short junctions (acceptor):  0\nNumber of junctions contains N:        0\nNumber of accepted junctions:          8764\nThe First 10 Splicing Signals Types: \nSignal  Forward  Reverse  Percentage\n  GTAG     4096     4431       97.30\n  AAAA       18       17        0.40\n  TATA       12        8        0.23\n  GCAG        9        9        0.21\n  TTTT        6        9        0.17\n  ATAT        4        7        0.13\n  GAGA        5        6        0.13\n  AGAG        3        6        0.10\n  TATT        3        6        0.10\n  TAAT        4        5        0.10\n[2025-07-18 12:52:47]   [LOG]   Collecting splice junctions from SAM/BAM file '/outputdir/test_run_10K_gsnap.bam'\n[2025-07-18 12:52:47]   [INFO]  Sense junctions 518\n[2025-07-18 12:52:47]   [INFO]  Antisense junctions 551\n[2025-07-18 12:52:47]   [INFO]  Total number of reads 20479\n[2025-07-18 12:52:47]   [INFO]  Total number of spliced reads 2233 10.903852727183946%\n[2025-07-18 12:52:47]   [LOG]   Finished parsing a SAM file, len(found_junctions_table)= 1069\n[2025-07-18 12:52:47]   [LOG]   Generating splice-junction prediction dataset batch: 1\n[2025-07-18 12:52:47]   [LOG]   Writting dev.csv file for predicting into '/outputdir/test_run_10K_prediction_batch_1/'\n[2025-07-18 12:52:47]   [LOG]   dev.csv file contains:   0: 1069, 1: 1069\n[2025-07-18 12:52:47]   [LOG]   Predicting found splice junctions using DNABERT MS150\n100%|██████████| 67/67 [00:01<00:00, 58.23it/s]\n[2025-07-18 12:52:51]   [LOG]   Generating genome regions \n[2025-07-18 12:52:51]   [LOG]   Parsing FASTA file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa'\n[2025-07-18 12:52:53]   [LOG]   Finished writing BAM successfully into '/outputdir/test_run_10K'\n[2025-07-18 12:52:53]   [LOG]   Number of SAM records: 20479 \n[2025-07-18 12:52:53]   [LOG]   Number of reads IDs:   12644 \n[2025-07-18 12:52:53]   [LOG]   Number of processed reads IDs: 1405  11.11% \n\n[2025-07-18 12:52:54]   [LOG]   Finished successfully\n

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Command-line Arguments

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Argument	Description	Required	Default
`--mode`	Pipeline mode to run: `index` or `GSNAP+TSJS`. See Pipeline Modes.	No	`GSNAP+TSJS`
`-o, --out`	Path to the output folder	Yes	—
`--prefix`	Output files prefix string	Yes	—
`-g, --gtf`	Path to the GTF annotation file compatible with the BAM file	Yes	—
`-f, --fasta`	Path to the FASTA genome file compatible with the BAM file	Yes	—
`-s, --sam`	Path to the SAM/BAM file or directory of files	Yes (if BAM)	—
`--mate_1`	Path to FASTQ file of mate 1 (for paired-end reads)	Yes (if FASTQ)	—
`--mate_2`	Path to FASTQ file of mate 2 (for paired-end reads)	Yes (if FASTQ)	—
`--gsnap_idx`	Path to GSNAP index. If omitted in `GSNAP+TSJS` mode, one is auto-built from `--fasta`+`--gtf`.	No	auto-build at `<out>/gsnap_idx/`
`--gsnap_idx_flags`	Extra flags passed to `gmap_build` and `gsnap`	No	`-d index -c transcriptome`
`--gsnap_aln_flags`	Extra flags passed to `gsnap` at alignment time. See GSNAP accelerated parameters below for GPU-acceleration knobs you can wire in here.	No	`--gunzip -A sam --novelsplicing 1`
`-c, --config`	Config `.json` file to control DeepSAP internal parameters	No	`/scripts/parameters_config.json`
`--batch`	Number of candidate splice junctions scored per transformer forward pass. Larger values raise throughput but increase GPU memory use (see Requirements for a memory-vs-batch reference).	No	`2048`
`--score_method`	Scoring method applied to junctions and reads: `Add` or `Multiply`	No	`Add`
`--no-fp16`	Don't use fp16 half-precision floating-point	No	fp16 enabled
`--set_size`	Set size to split datasets for inference	No	`102400` (= 1024 × 100)
`-t, --threads`	Number of threads	No	host `os.cpu_count()`
`--localdb-batch`	[GSNAP accelerated, passed through to `gsnap` only if set] Requests packed into each GPU kernel launch on the accelerated `--localdb=GPU` path.	No	unset (gsnap default `24000`)
`--localdb-scratch`	[GSNAP accelerated, passed through to `gsnap` only if set] Unified GPU device-byte budget for localdb scratch (accepts `K`/`M`/`G` suffixes, e.g. `8G`).	No	unset (gsnap default `12G`)
`--batch-nreads`	[GSNAP accelerated, passed through to `gsnap` only if set] Max individual reads per frame. Paired-end input requires an even value ≥ 2.	No	unset (gsnap default `250`)

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Version History

v0.1.0

Added GPU-accelerated GSNAP v2025-04-19. The runtime image now ships a CUDA-accelerated GSNAP build with both Stage-1 (r2d) and Stage-2 (localdb) running on the GPU by default; tunable passthrough knobs are exposed via --localdb-batch, --localdb-scratch, and --batch-nreads (see Command-line Arguments).
Added --mode flag to explicitly select pipeline mode (index, GSNAP+TSJS). The default GSNAP+TSJS preserves the v0.0.x end-to-end behaviour, including auto-building a GSNAP index when --gsnap_idx is omitted.
Bug fix: output BAM is now correctly suffixed with .bam.
Bug fix: SAM records with empty CIGAR strings are now normalised to * before being written to the BAM stream.
Bug fix: stricter logits-shape validation in predict.py (previously masked by a bare except).

v0.0.3

Fixed key error in parsing FASTA files.
Fixed gene_id pattern error in parsing GTF files.

v0.0.2

Updated GSNAP aligner to version 2025-04-19.

v0.0.1

Initial release.

Get Help

For questions, bug reports, or other DeepSAP support requests, please use the Parabricks developer forum.

License/Terms of Use

By pulling and using the Parabricks DeepSAP container, you accept the governing terms: The software and materials are governed by the NVIDIA Software License Agreement (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the Product-Specific Terms for NVIDIA AI Products (found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); except for the model which is governed by the NVIDIA Models Community License Agreement(found at NVIDIA Community Model License). ADDITIONAL INFORMATION: Apache 2.0.

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\n\n\nWe evaluated the performance of DeepSAP in our *Genome Biology* article: [***DeepSAP: improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring***](https://doi.org/10.1186/s13059-026-04100-3) (Berakdar, Wu, Zhu, Samadi, Vats, 2026). In our benchmark, DeepSAP demonstrated strong performance, achieving consistently outstanding results across all evaluated metrics using Baruzzo et al. datasets. For additional resources, including data, detailed analyses, and other supplementary materials accompanying the **DeepSAP** article, please refer to the [DeepSAP GitHub repository](https://github.com/clara-parabricks-workflows/DeepSAP/tree/main).\n\n\n## Table of Contents\n- [Requirements](#requirements)\n- [Usage](#usage)\n- [Pipeline Modes](#pipeline-modes)\n- [Command-line Arguments](#command-line-arguments)\n- [Version History](#version-history)\n- [Get Help](#get-help)\n- [License/Terms of Use](#licenseterms-of-use)\n\n## Requirements\n### System Software:\n* Docker with GPU support
\n### System Hardware:\nSizing below is for a human genome–scale reference (GRCh38). The two pipeline stages run sequentially, so peak GPU memory is the **maximum** of the alignment-stage and TSJS-stage footprints — not their sum.\n\n**CPU & RAM:**\n* **CPU:** 24 cores recommended (drives GSNAP's pipeline-parallel stages — reader / solver / writer threads — and DeepSAP's TSJS scoring stage).\n* **System RAM:** 64 GB minimum.\n\n**GPU memory:**\n* **Minimum recommended:** 40 GB (validated on NVIDIA A100 PCIe 40 GB, H100 PCIe, and RTX A6000 48 GB).\n* The alignment stage sets the floor; the TSJS stage's GPU memory scales with `--batch` and `--fp16`.\n\n*Alignment stage (GPU-accelerated GSNAP):*\n* GSNAP transcriptome-guided genome index resident on device: **~24 GB**.\n* `--localdb-scratch` (Stage-2 localdb GPU scratch buffer): default **`12G`**, tunable.\n* **Default total: ~36 GB.** Setting `--localdb-scratch=1G` brings the alignment-stage footprint down to **~25 GB** (fits a 24 GB card with little headroom — closer to 32 GB is comfortable).\n\n*TSJS (transformer splice-junction scoring) stage:*\n\nGPU memory here is dominated by two parameters:\n* **`--batch`**: number of candidate splice junctions scored per transformer forward pass. Larger batches significantly improve throughput but require more GPU memory.\n* **`--fp16`**: half-precision floating-point inference is enabled by default and roughly halves GPU memory versus fp32. Disable with `--no-fp16`, which approximately doubles the per-batch memory shown below.\n\n | **`--batch`** | **Approximate GPU memory (with `--fp16`)** |\n | :------------ | :----------------------------------- |\n | 64 | ~1.2 GB |\n | 128 | ~1.6 GB |\n | 256 | ~2.2 GB |\n | 2048 (default)| ~10.4 GB |\n | 8192 | ~39.5 GB |\n\n### Input Data:\n* RNA-seq reads in FASTQ format.\n* Reference file in FASTA format.\n* Annotation file in GTF format.\n* Optionally, a path to a GSNAP index.\n\n## Usage\nThis guide demonstrates how to quickly test DeepSAP's functionality using the **`malaria_short_pe`** dataset. Follow these steps to set up your environment and run DeepSAP:\n\n### Least-privilege container execution\n\nFor production runs, mount only the input, index, and output directories that DeepSAP needs. Keep reference data, FASTQ/BAM inputs, and reusable indexes read-only; use a separate writable output directory for results.\n\n### Step 1: Prepare Environment and Download Test Data\nThis step downloads the latest DeepSAP Docker container and all required reference files and test sequencing data.\n\n```bash\n# Pull the DeepSAP Parabricks Docker image\ndocker pull nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest\n\n# Create writable result directories as the invoking host user\nmkdir -p test/outputdir test/results/prebuilt test/results/rescored\n\n# Download reference genome and annotation files\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n\n# Download downsampled FASTQ sequence reads (10K) from DeepSAP GitHub\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/SRR14793977_10K_1.fastq.gz\nwget -P test/malaria_short_pe/ https://raw.githubusercontent.com/clara-parabricks-workflows/DeepSAP/main/test/malaria_short_pe/SRR14793977_10K_2.fastq.gz\n```\n\n### Step 2: Build a GSNAP Index (`--mode index`)\nThis command builds a standalone, reusable GSNAP TGGA index from the FASTA + GTF and writes it under `//`. Useful when you plan to score many samples against the same reference — build the index once, then reuse it in Step 4.\n\n```bash\n# Build a reusable GSNAP index from the malaria reference\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode index \\\n --out /outputdir/ \\\n --prefix malaria_idx \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n```\n\n### Step 3: Run DeepSAP End-to-End (auto-build index, `--mode GSNAP+TSJS`)\nThis command executes the full DeepSAP pipeline (GSNAP alignment + transformer splice-junction scoring) on the downloaded test dataset using the default `--mode GSNAP+TSJS`. Since `--gsnap_idx` is not specified, DeepSAP auto-builds a GSNAP index inline at `/gsnap_idx/` before alignment. Pick this path for one-shot runs where you don't need to reuse the index later.\n\n```bash\n# Run DeepSAP end-to-end (GSNAP index will be auto-generated)\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode GSNAP+TSJS \\\n --out /outputdir/ \\\n --prefix test_run_10K \\\n --mate_1 /workdir/malaria_short_pe/SRR14793977_10K_1.fastq.gz \\\n --mate_2 /workdir/malaria_short_pe/SRR14793977_10K_2.fastq.gz \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n```\n\n### Step 4: Run DeepSAP with a Pre-existing GSNAP Index (`--mode GSNAP+TSJS` + `--gsnap_idx`)\nIf you have already generated a GSNAP index (e.g., from Step 2, a previous DeepSAP run, or shared infrastructure), point DeepSAP at it via `--gsnap_idx`. This skips index construction, runs the same GSNAP alignment pipeline as an auto-indexed run, writes `_gsnap.bam`, and then applies TSJS scoring to that BAM.\n\n```bash\n# Run DeepSAP using the index built in Step 2\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir/malaria_idx:/gsnap_idx:ro\" \\\n --volume \"$(pwd)/test/results/prebuilt:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode GSNAP+TSJS \\\n --out /outputdir/ \\\n --prefix test_run_10K \\\n --mate_1 /workdir/malaria_short_pe/SRR14793977_10K_1.fastq.gz \\\n --mate_2 /workdir/malaria_short_pe/SRR14793977_10K_2.fastq.gz \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\\\n --gsnap_idx /gsnap_idx/\n```\n\n### Step 5: Score an Existing BAM with TSJS Only (`--mode GSNAP+TSJS` + `--sam`)\nIf you already have a GSNAP-aligned BAM (e.g., from a prior GSNAP alignment run, or from any other aligner whose SAM records carry the `XA` (alternative alignments) tag), pass it via `--sam` and DeepSAP skips alignment entirely — running transformer splice-junction scoring directly on the BAM. The output is a new BAM with TSJS-derived MAPQ adjustments and junction-scoring metadata.\n\n```bash\n# Score a pre-aligned BAM (no GSNAP step)\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir/test_run_10K_gsnap.bam:/input.bam:ro\" \\\n --volume \"$(pwd)/test/results/rescored:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode GSNAP+TSJS \\\n --out /outputdir/ \\\n --prefix test_run_10K_rescored \\\n --sam /input.bam \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n```\n\n> Note: `--sam` and `--mate_1`/`--mate_2` are mutually exclusive — DeepSAP either aligns *or* scores an existing alignment, never both in the same run.\n\n\n\n\n## Pipeline Modes\n\nDeepSAP's `--mode` flag selects which pipeline mode to run. The default `GSNAP+TSJS` reproduces the v0.0.x end-to-end behavior; `index` lets you pre-build a GSNAP index in isolation (useful for sharing a pre-built index across many samples).\n\n| `--mode` | Required inputs | Optional inputs | Outputs |\n|---|---|---|---|\n| `index` | `--fasta`, `--gtf` | — | GSNAP index at `//` |\n| `GSNAP+TSJS` (default) | `--fasta`, `--gtf`, **and** either `--mate_1`+`--mate_2` (optionally with `--gsnap_idx`) **or** `--sam` | model / batching flags, `--score_method` | scored BAM at `/.bam` (+ intermediate datasets) |\n\n### Mode 1: Build a GSNAP index only\n\n```bash\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode index \\\n --out /outputdir/ \\\n --prefix malaria_idx \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n```\n\n### Mode 2: Score an existing BAM with TSJS only\n\n```bash\ndocker run --gpus 1 --ulimit memlock=-1 --ulimit stack=67108864 --rm \\\n --user \"$(id -u):$(id -g)\" \\\n --cap-drop=ALL --security-opt=no-new-privileges \\\n --volume \"$(pwd)/test:/workdir:ro\" \\\n --volume \"$(pwd)/test/outputdir/test_run_10K_gsnap.bam:/input.bam:ro\" \\\n --volume \"$(pwd)/test/results/rescored:/outputdir:rw\" \\\n nvcr.io/nvidia/clara/clara-parabricks-deepsap:latest \\\n --mode GSNAP+TSJS \\\n --out /outputdir/ \\\n --prefix test_run_10K_rescored \\\n --sam /input.bam \\\n --gtf /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf \\\n --fasta /workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa\n# -> /outputdir/test_run_10K_rescored.bam (TSJS-scored)\n```\n\n### DeepSAP Expected Output\n```\n[2025-07-18 12:51:27] [INFO] Running DeepSAP v0.1.0\n[2025-07-18 12:51:32] [LOG] Running GSNAP\n[2025-07-18 12:51:32] [LOG] Building GSNAP TGGA index\n[2025-07-18 12:52:44] [LOG] Running GSNAP TGGA \n[2025-07-18 12:52:46] [LOG] Parsing FASTA file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa'\n[2025-07-18 12:52:46] [LOG] Parsing GTF file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.60.gtf'\n[2025-07-18 12:52:47] [LOG] Transcript information: \nNumber of transcripts: 5767\nShortest transcript: 67 EPT00050203058\nLongest transcript: 30863 CAG25094\nTranscripts length mean: 2456.79\nTranscripts length median: 1618\nTranscripts length mode: 71\nShortest intron: 1 PF3D7_1478200: 14__-__3219919__3220323 -> 14__-__3220325__3220534\nLongest intron: 2425 CZU00099: 14__+__1639681__1639728 -> 14__+__1642154__1642455\nIntrons length mean: 163.03\nIntrons length median: 141.0\nIntrons length mode: 1\nNumber of multi exons transcripts: 3064 53.13%\nNumber of mono exon transcripts: 2703 46.87%\n\nType of transcripts:\n BioType Count Percentage\n0 protein_coding 5358 92.91\n1 pseudogene 153 2.65\n3 ncRNA 102 1.77\n4 tRNA 79 1.37\n5 rRNA 44 0.76\n7 sRNA 17 0.29\n6 snRNA 10 0.17\n2 nontranslating_CDS 4 0.07\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from GTF\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions in mode=NotStrict and window=150\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from transcript types: All\nNumber of duplicated junctions: 328\nNumber of short junctions (intron): 0\nNumber of short junctions (donor): 0\nNumber of short junctions (acceptor): 0\nNumber of junctions contains N: 0\nNumber of accepted junctions: 8764\nThe First 10 Splicing Signals Types: \nSignal Forward Reverse Percentage\n GTAG 4096 4431 97.30\n AAAA 18 17 0.40\n TATA 12 8 0.23\n GCAG 9 9 0.21\n TTTT 6 9 0.17\n ATAT 4 7 0.13\n GAGA 5 6 0.13\n AGAG 3 6 0.10\n TATT 3 6 0.10\n TAAT 4 5 0.10\n[2025-07-18 12:52:47] [LOG] Collecting splice junctions from SAM/BAM file '/outputdir/test_run_10K_gsnap.bam'\n[2025-07-18 12:52:47] [INFO] Sense junctions 518\n[2025-07-18 12:52:47] [INFO] Antisense junctions 551\n[2025-07-18 12:52:47] [INFO] Total number of reads 20479\n[2025-07-18 12:52:47] [INFO] Total number of spliced reads 2233 10.903852727183946%\n[2025-07-18 12:52:47] [LOG] Finished parsing a SAM file, len(found_junctions_table)= 1069\n[2025-07-18 12:52:47] [LOG] Generating splice-junction prediction dataset batch: 1\n[2025-07-18 12:52:47] [LOG] Writting dev.csv file for predicting into '/outputdir/test_run_10K_prediction_batch_1/'\n[2025-07-18 12:52:47] [LOG] dev.csv file contains: 0: 1069, 1: 1069\n[2025-07-18 12:52:47] [LOG] Predicting found splice junctions using DNABERT MS150\n100%|██████████| 67/67 [00:01<00:00, 58.23it/s]\n[2025-07-18 12:52:51] [LOG] Generating genome regions \n[2025-07-18 12:52:51] [LOG] Parsing FASTA file '/workdir/malaria_short_pe/Plasmodium_falciparum.ASM276v2.dna.toplevel.fa'\n[2025-07-18 12:52:53] [LOG] Finished writing BAM successfully into '/outputdir/test_run_10K'\n[2025-07-18 12:52:53] [LOG] Number of SAM records: 20479 \n[2025-07-18 12:52:53] [LOG] Number of reads IDs: 12644 \n[2025-07-18 12:52:53] [LOG] Number of processed reads IDs: 1405 11.11% \n\n[2025-07-18 12:52:54] [LOG] Finished successfully\n```\n
\n\n## Command-line Arguments\n\n| Argument | Description | Required | Default |\n|-------------------|------------------------------------------------------------------------------------------------------|----------------|---------|\n| `--mode` | Pipeline mode to run: `index` or `GSNAP+TSJS`. See [Pipeline Modes](#pipeline-modes). | No | `GSNAP+TSJS` |\n| `-o, --out` | Path to the output folder | Yes | — |\n| `--prefix` | Output files prefix string | Yes | — |\n| `-g, --gtf` | Path to the GTF annotation file compatible with the BAM file | Yes | — |\n| `-f, --fasta` | Path to the FASTA genome file compatible with the BAM file | Yes | — |\n| `-s, --sam` | Path to the SAM/BAM file or directory of files | Yes (if BAM) | — |\n| `--mate_1` | Path to FASTQ file of mate 1 (for paired-end reads) | Yes (if FASTQ) | — |\n| `--mate_2` | Path to FASTQ file of mate 2 (for paired-end reads) | Yes (if FASTQ) | — |\n| `--gsnap_idx` | Path to GSNAP index. If omitted in `GSNAP+TSJS` mode, one is auto-built from `--fasta`+`--gtf`. | No | auto-build at `/gsnap_idx/` |\n| `--gsnap_idx_flags` | Extra flags passed to `gmap_build` and `gsnap` | No | `-d index -c transcriptome` |\n| `--gsnap_aln_flags` | Extra flags passed to `gsnap` at alignment time. See [GSNAP accelerated parameters](#gsnap-accelerated-parameters) below for GPU-acceleration knobs you can wire in here. | No | `--gunzip -A sam --novelsplicing 1` |\n| `-c, --config` | Config `.json` file to control DeepSAP internal parameters | No | `/scripts/parameters_config.json` |\n| `--batch` | Number of candidate splice junctions scored per transformer forward pass. Larger values raise throughput but increase GPU memory use (see [Requirements](#requirements) for a memory-vs-batch reference). | No | `2048` |\n| `--score_method` | Scoring method applied to junctions and reads: `Add` or `Multiply` | No | `Add` |\n| `--no-fp16` | Don't use fp16 half-precision floating-point | No | fp16 enabled |\n| `--set_size` | Set size to split datasets for inference | No | `102400` (= 1024 × 100) |\n| `-t, --threads` | Number of threads | No | host `os.cpu_count()` |\n| `--localdb-batch` | [GSNAP accelerated, passed through to `gsnap` only if set] Requests packed into each GPU kernel launch on the accelerated `--localdb=GPU` path. | No | unset (gsnap default `24000`) |\n| `--localdb-scratch` | [GSNAP accelerated, passed through to `gsnap` only if set] Unified GPU device-byte budget for localdb scratch (accepts `K`/`M`/`G` suffixes, e.g. `8G`). | No | unset (gsnap default `12G`) |\n| `--batch-nreads` | [GSNAP accelerated, passed through to `gsnap` only if set] Max individual reads per frame. Paired-end input requires an even value ≥ 2. | No | unset (gsnap default `250`) |\n---------------------------------------------------------------------------------------------------------------------------------------------\n
\n\n## Version History\n### v0.1.0\n* Added GPU-accelerated GSNAP v2025-04-19. The runtime image now ships a CUDA-accelerated GSNAP build with both Stage-1 (r2d) and Stage-2 (localdb) running on the GPU by default; tunable passthrough knobs are exposed via `--localdb-batch`, `--localdb-scratch`, and `--batch-nreads` (see [Command-line Arguments](#command-line-arguments)).\n* Added `--mode` flag to explicitly select pipeline mode (`index`, `GSNAP+TSJS`). The default `GSNAP+TSJS` preserves the v0.0.x end-to-end behaviour, including auto-building a GSNAP index when `--gsnap_idx` is omitted.\n* Bug fix: output BAM is now correctly suffixed with `.bam`.\n* Bug fix: SAM records with empty CIGAR strings are now normalised to `*` before being written to the BAM stream.\n* Bug fix: stricter logits-shape validation in `predict.py` (previously masked by a bare `except`).\n### v0.0.3\n* Fixed key error in parsing FASTA files.\n* Fixed gene_id pattern error in parsing GTF files. \n### v0.0.2\n* Updated GSNAP aligner to version `2025-04-19`.\n### v0.0.1\n* Initial release.\n\n## Get Help\n\nFor questions, bug reports, or other DeepSAP support requests, please use the [Parabricks developer forum](https://forums.developer.nvidia.com/c/healthcare/parabricks/).\n\n## License/Terms of Use\nBy pulling and using the Parabricks DeepSAP container, you accept the governing terms: The software and materials are governed by the NVIDIA Software License Agreement (found at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the Product-Specific Terms for NVIDIA AI Products (found at https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); except for the model which is governed by the NVIDIA Models Community License Agreement(found at NVIDIA Community Model License). ADDITIONAL INFORMATION: Apache 2.0.","DeepSap",["D",1785952082802],[250,248,246,188,252],"/api/image/nvidia/clara/containers/clara-parabricks-deepsap",12588695821,"hasSignedTag",[294],[276,277,278,279],[],[272],[],{"_91":273,"_123":273,"_124":274,"_126":275},"signed images",{"_128":275,"_123":273},"system",{"_91":290,"_123":291,"_124":292,"_126":127},{"_91":286,"_123":287,"_124":288,"_126":127},{"_91":282,"_123":283,"_124":284,"_126":127},{"_91":280,"_123":280,"_124":281,"_126":127},"NSPECT-CWKM-PN83",{"_128":127,"_123":280},"Life Sciences","indus_life_sciences",{"_128":285,"_123":283},"industry","Genomics","soln_genomics",{"_128":289,"_123":287},"technology","Genome Sequencing","uscs_genome_sequencing",{"_128":293,"_123":291},"application",{"_91":295,"_123":295,"_124":296,"_126":297},"NVIDIA AI Enterprise Supported",{"_128":127,"_123":295},"feature",{"_155":1272,"_157":1273},"ngcApps",[1225],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1206":1207,"_1208":1209,"_1210":1211,"_1212":1213,"_1214":309,"_187":335,"_1215":1216,"_1217":51,"_1218":134,"_1219":134,"_1220":1221,"_1222":134,"_1223":1224},"amplitude",{"_1203":1204,"_848":-7,"_1205":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1197":1198,"_1199":1200,"_1201":1202},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787640500307,"1.12.0","defaultSearchSuggestions",{"_1088":415,"_1089":1090,"_1091":1092,"_1093":1094},"announcements",[],"consentState",{"_373":1078,"_1079":134,"_1080":26,"_1081":26,"_1082":1083},"consentConfig",{"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374},"showContactAdmin",[],[],[348,349,350,351,352,353,354],{"_40":61,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":62,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":55,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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DeepSAP

Table of Contents

Requirements

System Software:

System Hardware:

Input Data:

Usage

Least-privilege container execution

Step 1: Prepare Environment and Download Test Data

Step 2: Build a GSNAP Index (--mode index)

Step 3: Run DeepSAP End-to-End (auto-build index, --mode GSNAP+TSJS)

Step 4: Run DeepSAP with a Pre-existing GSNAP Index (--mode GSNAP+TSJS + --gsnap_idx)

Step 5: Score an Existing BAM with TSJS Only (--mode GSNAP+TSJS + --sam)

Pipeline Modes

Mode 1: Build a GSNAP index only

Mode 2: Score an existing BAM with TSJS only

DeepSAP Expected Output

Command-line Arguments

Version History

v0.1.0

v0.0.3

v0.0.2

v0.0.1

Get Help

License/Terms of Use

Step 2: Build a GSNAP Index (`--mode index`)

Step 3: Run DeepSAP End-to-End (auto-build index, `--mode GSNAP+TSJS`)

Step 4: Run DeepSAP with a Pre-existing GSNAP Index (`--mode GSNAP+TSJS` + `--gsnap_idx`)

Step 5: Score an Existing BAM with TSJS Only (`--mode GSNAP+TSJS` + `--sam`)