Jumper

(a) Viruses in the order Nidovirales generate a set of discontinuous transcripts with varying abundances during infection. (b) Next generation sequencing will produce an alignment with two types of aligned reads: unphased reads that map to a contiguous genomic region (black) and phased reads that map to distinct genomic regions (red). (c) From we obtain the segment graph , a directed acyclic graph with a unique Hamiltonian path. Jumper solves the Discontinuous Transciption Assembly problem to infer and with maximum likelihood.

More details about the problem formulation and the algorithm can be found here: https://www.nature.com/articles/s41467-021-26944-y

Pre-requisites
Installation
- Using conda (recommended)
- Using pip (alternative)
Usage instcructions

Pre-requisites

python3 (>=3.6)
numpy
pysam
pandas
gurobipy
(optional for simulation pipeline) snakemake (>=5.2.0)

Installation

Installation time: 5 minutes.

Using conda (recommended)

Download the released package of the latest version: jumper-0.1.1.tar.bz2.

Create a new conda environment named "jumper" and install dependencies:

If you want to run the simulation pipeline, please run

conda create -n jumper -c conda-forge -c bioconda -c gurobi pandas pysam snakemake STAR scallop stringtie gurobi

Otherwise please run

conda create -n jumper -c conda-forge -c bioconda -c gurobi pandas pysam gurobi

Then activate the created environment: conda activate jumper.
Install the package into current environment "jumper":
```
conda install jumper-0.1.1.tar.bz2
```

Note: Make sure you have a gurobi license before running jumper. If you are an academic user, you can get a free license: https://www.gurobi.com/academia/academic-program-and-licenses/

Using pip (alternative)

For users not having conda, and already have gurobi installed:

Clone the git repository

git clone [email protected]:elkebir-group/Jumper.git

Install jumper using pip
```
cd Jumper
pip install ./
```

Usage instructions

I/O formats

The input for Jumper is a bam file containing the sequencing data and a fasta file containing the reference genome. The output is similar to a fasta file format, where each transcript name is followed by the edges in the corresponding path in the segment graph (see data/sample_transcripts.out for an example).

Arguments

usage: jumper_main.py [-h] [-b BAM] [--paired PAIRED] -f FASTA [-k NUMPATHS]
                      [--min-base-qual MIN_BASE_QUAL]
                      [--min-mapping-qual MIN_MAPPING_QUAL] [-w WIDTH]
                      [--samplingFrequency SAMPLINGFREQUENCY]
                      [--sj_threshold SJ_THRESHOLD] [-n NEDGES]
                      [--phasing_threshold PHASING_THRESHOLD]
                      [--greedy GREEDY] [--outputCSV OUTPUTCSV]
                      [--outputPhasing OUTPUTPHASING] [--inputCSV INPUTCSV]
                      [--inputPhasing INPUTPHASING]
                      [--inputBreakpoints INPUTBREAKPOINTS]
                      [--inputEdges INPUTEDGES] [--outputGraph OUTPUTGRAPH]
                      [--outputDOT OUTPUTDOT]
                      [--outputTranscripts OUTPUTTRANSCRIPTS]
                      [--outputBreakpoints OUTPUTBREAKPOINTS]
                      [--outputEdges OUTPUTEDGES]
                      [--outputDecomposition OUTPUTDECOMPOSITION]
                      [--outputMatching OUTPUTMATCHING]
                      [--outputGTF OUTPUTGTF] [--report REPORT] [--noverbose]
                      [--threads THREADS] [--timelimit TIMELIMIT]
                      [--maxIter MAXITER]

optional arguments:
  -h, --help            show this help message and exit
  -b BAM, --bam BAM     aligned bam file
  --paired PAIRED       is the bam file paired-end (True/False) [True]
  -f FASTA, --fasta FASTA
                        fasta file
  -k NUMPATHS           number of paths for the flow decomposition
  --min-base-qual MIN_BASE_QUAL
                        minimum base quality [20]
  --min-mapping-qual MIN_MAPPING_QUAL
                        minimum mapping quality [20]
  -w WIDTH, --width WIDTH
                        spliced junction width parameter [0]
  --samplingFrequency SAMPLINGFREQUENCY
                        number of sampling points for the likelihood function
  --sj_threshold SJ_THRESHOLD
                        minimum support for splicing junction [20]
  -n NEDGES, --nedges NEDGES
                        number of splice edges in segment graph (-1 for
                        unconstrained) [-1]
  --phasing_threshold PHASING_THRESHOLD
                        coverage threshold for transcripts [0]
  --greedy GREEDY       set greedy flag to TRUE
  --outputCSV OUTPUTCSV
                        output csv file for sj reads
  --outputPhasing OUTPUTPHASING
                        output file containing phasing reads
  --inputCSV INPUTCSV   input csv file with sj reads
  --inputPhasing INPUTPHASING
                        input phasing file
  --inputBreakpoints INPUTBREAKPOINTS
                        input file containing breakpoints
  --inputEdges INPUTEDGES
                        input file containing graph edges
  --outputGraph OUTPUTGRAPH
                        output graph file
  --outputDOT OUTPUTDOT
                        output DOT file for splice graph
  --outputTranscripts OUTPUTTRANSCRIPTS
                        output file for transcripts
  --outputBreakpoints OUTPUTBREAKPOINTS
                        output file containing breakpoints
  --outputEdges OUTPUTEDGES
                        output file containing graph edges
  --outputDecomposition OUTPUTDECOMPOSITION
                        output file for the decomposed non-canonical
                        transcripts
  --outputMatching OUTPUTMATCHING
                        output file for the matching of phasing reads to
                        inferred transcripts
  --outputGTF OUTPUTGTF
                        output file in GTF format
  --report REPORT       output file for report on the splice graph
  --noverbose           do not output statements from internal solvers
                        [default is false]
  --threads THREADS     number of threads allowed to be used [1]
  --timelimit TIMELIMIT
                        time limt for the gurobi solvers in seconds [None]
  --maxIter MAXITER     maximum iterations for the greedy algorithm [100]

Example

One way to see how to use Jumper is through simulation_pipeline for example cases of using Jumper on simulated bam files. The Jumper usage is shown in the snakemake simulation_pipeline/jumper.smk.

Here we will run Jumper to reconstruct the transcripts on simulated phasing reads. You'll need to download this repository to get the resources files in the data/ folder.

Simulate transcripts and phasing reads

$ jumper_simulate --seed 0 --sense neg --npaths 2 --inputBreakpoints ../data/sampleBreakpoints.out --inputEdges ../data/sampleEdges.out --outputPaths ../data/sample_transcripts.out --outputFasta ../data/sample_transcripts.fasta -f ../data/reference.fasta --outputReadCounts ../data/sample_readcounts.out --outputPhasing ../data/sample_phasing.out --nreads 1000

This command generates 1000 phasing reads in ../data/sample_phasing. The ground transcripts are written in ../data/sample_transcripts.out.

Reconstruct the transcripts

$ jumper --inputBreakpoints ../data/sampleBreakpoints.out --inputEdges ../data/sampleEdges.out --inputPhasing ../data/sample_phasing.out --outputDecomposition ../data/sample_decomposition.out -k 50 -f ../data/reference.fasta --greedy True --outputMatching ../data/sample_matching.out > ../data/sample.log

The reconstructed transcripts are written to ../data/sample_decomposition.out.

This will take less than 1 minute to complete on a typical desktop computer.

elkebir-group / jumper Goto Github PK

jumper's Introduction

Jumper

Contents

Pre-requisites

Installation

Using conda (recommended)

Using pip (alternative)

Usage instructions

I/O formats

Arguments

Example

Simulate transcripts and phasing reads

Reconstruct the transcripts

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