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bcalm

(★ 101)

compacted de Bruijn graph construction in low memory

파일 탐색기

최종 버전 다운로드 (.zip)

- config.yml
- bidirected-graphs-in-bcalm2.md
- fig1.png
- fig2.png
- fig33.png
- fig4.png
- fig5.png
- fig66.png
- fig7.png
- fig8.png
- figs.pptx
- - CMD
  - README
  - test1.fa
  - test2.fa
  - test3.fa
- - refs.fa
  - run.sh
- - Makefile
  - testUF.cpp
- run-tiny.sh
- tiny_read.fa
- abundance_stats.py
- convertToGFA.py
- memused
- pufferize.py
- release.sh
- split_unitigs.py
- unitigEvaluator.cpp
- bcalm_1.cpp
- bcalm_1.hpp
- main.cpp
- minitip.fa
- simple_test.sh
- concurrentqueue.h
- lockbasedqueue.h
- lockstdqueue.h
- lockstdvector.h
- ThreadPool.h
.gitignore
.gitmodules
.travis.yml
CMakeLists.txt
gatb-core
LICENSE
README.md
VERSION

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/GATB/bcalm

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd bcalm

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. CMake

보통 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
CMake 빌드 설정을 생성하는 도구입니다.
C/C++ 컴파일러 Windows는 Visual Studio(Community 무료), macOS는 Xcode Command Line Tools, Linux는 gcc/g++ 패키지가 필요합니다.

mkdir build; cd build; cmake ..; make -j 8

빌드 결과물을 담을 폴더를 만들고 그 안으로 이동합니다.

rm -Rf CMake* && cmake -DKSIZE_LIST="32 64 96 128 160 192 224 256 320" .. && make -j 8

이 명령어를 터미널에 그대로 입력해 실행하세요.

✅ build 폴더 안에 실행 파일이 생성됐는지 확인하고, 직접 실행해보세요 (예: ./build/앱이름).

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# BCALM 2

BCALM 2 is a bioinformatics tool for constructing the compacted de Bruijn graph from sequencing data.

[![Build Status](https://travis-ci.org/GATB/bcalm.svg?branch=master)](https://travis-ci.org/GATB/bcalm)

# Usage

Read the instructions below to compile, then:

./bcalm -in [reads.fa] -kmer-size [kmer_size] -abundance-min [abundance_threshold]
  
e.g.

./bcalm -in reads.fastq -kmer-size 21 -abundance-min 2

Importants parameters are:

-kmer-size [int]
    
The k-mer size, i.e. length of the nodes of the de Bruijn graph.

-abundance-min [int]

Sets a threshold X below which k-mers that are seen (strictly) less than X times in the dataset are filtered out; i.e. sequencing errors, typically.

# Pre-requisites:

GCC >= 4.8 or a very recent C++11 capable compiler

# Installation

Download the latest [Linux/MacOS binaries](https://github.com/GATB/bcalm/releases), or compile from source as follows:

git clone --recursive https://github.com/GATB/bcalm 
    cd bcalm
    mkdir build;  cd build;  cmake ..;  make -j 8
    
You can also install bcalm from [bioconda](https://bioconda.github.io/) with [conda](https://docs.conda.io/en/latest/):

conda install -c conda-forge -c bioconda bcalm

# Input formats

File input format can be fasta, fastq, either gzipped or not. BCALM 2 does not care about paired-end information, all given reads contribute to k-mers in the graph (as long as such k-mers pass the abundance threshold).

To pass several files as input:

ls -1 *.fastq > list_reads
    ./bcalm -in list_reads [..]
   
# Output

BCALM 2 outputs the set of unitigs of the de Bruijn graph.
A unitig is the sequence of a non-branching path. Unitigs that are connected by an edge in the graph overlap by exactly (k-1) nucleotides. For a formal description of what BCALM2 outputs, see [here](bidirected-graphs-in-bcalm2/bidirected-graphs-in-bcalm2.md)

We have two output formats: FASTA and GFA.

**GFA** output: use `scripts/convertToGFA.py` to convert the output of BCALM 2 to GFA (contributed by Mayank Pahadia).

FASTA output header:

><id> LN:i:<length> KC:i:<abundance> km:f:<abundance> L:<+/->:<other id>:<+/-> [..]

Where:

* `LN` field is the length of the unitig
    
* `KC` and `km` fields are for total abundance and mean abundance of kmers inside the unitig, respectively.

* Edges between unitigs are reported as `L:x:y:z` entries in the FASTA header (1 entry per edge). A classic forward-forward outcoming edge is labeled `L:+:[next node]:+`. A forward-reverse, `L:+:[next node]:-`. Incoming edges are encoded as outcoming edges of the reverse-complement node. E.g. `L:-:[previous node]:+` means that if you reverse-complemented the current node, then there would be an edge from the last k-mer of current node to the first k-mer of the forward strand of [previous node].

FASTA output header with `-all-abundance-counts`:

><id> LN:i:<length> ab:Z:<abundance_0> <abundance_(length-k)>  L:<+/->:<other id>:<+/-> [..]
    
Where:

* `ab` field is a vector containing all the kmer abundance in the unitig 
    
# Reverse-complements and double-strandedness

BCALM 2 converts all k-mers into their canonical representation with respect to reverse-complements.
In other words, a k-mer and its reverse complement are considered to be the same object, appearing only once in the output, either in forward or reverse orientation.

Note: in the output of BCALM 2, each unitig may be either be returned in forward or reverse orientation, with no guarantee that the orientation will stay the same across identical runs of the software.

For a formal description of how BCALM2 handles double-strandedness of DNA, see [here](bidirected-graphs-in-bcalm2/bidirected-graphs-in-bcalm2.md)

# Larger k values

BCALM 2 supports arbitrary large k-mer lengths. You need to recompile it from sources. For k up to, say, 320, type this in the build folder:

rm -Rf CMake* && cmake -DKSIZE_LIST="32 64 96 128 160 192 224 256 320" .. && make -j 8

For compilation, list of kmers should only contain multiples of 32. Also, for technical reason, keep 32 in the list. Of course, for higher k's, BCALM will run slower. Intermediate values create optimized code for smaller $k$'s. You could specify just `KSIZE_LIST="32 320"` but then using k values above 32 would be as slow as if k was equal to 320.

After that, BCALM 2 can be run with any k value up to the largest one specified during compilation.

# Intermediate files

BCALM 2 produces some intermediate files: a .h5 file (or a _gatb/ folder), which contain the k-mer counts. The "\*glue\*" files contain compacted sequences that needs to be glued together (see BCALM 2 paper). Those files can be safely deleted after an execution, as the actual output is just the FASTA file containing the unitigs.

Acknowledgements
========
If using BCALM 2, please cite:
Rayan Chikhi, Antoine Limasset and Paul Medvedev, Compacting de Bruijn graphs from sequencing data quickly and in low memory, Proceedings of ISMB 2016, Bioinformatics, 32 (12): i201-i208.  ([Bibtex](https://academic.oup.com/Citation/Download?resourceId=2289008&resourceType=3&citationFormat=2))

This project has been supported in part by NSF awards DBI-1356529, CCF-1439057, IIS-1453527, and IIS-1421908.

The legacy, single-threaded code of BCALM version 1 is still available at: https://github.com/Malfoy/bcalm