BOL: Related items

Integrative Meta-Assembly Pipeline (IMAP): Chromosome-level genome assembler combining multiple de novo assemblies

Jit — Sat, 31 Aug 2019 11:30:41 -0500

Chromosome-level genome assembler combining multiple de novo assemblies

Address of the bookmark: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221858

Liftoff: an accurate tool that maps annotations in GFF or GTF between assemblies

Jit — Tue, 30 Jun 2020 21:40:52 -0500

Liftoff, an accurate tool that maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.

Address of the bookmark: https://github.com/agshumate/Liftoff

DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication

Jit — Tue, 14 Nov 2017 10:26:16 -0600

We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7,000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 minutes, with rich information such as pseudogenes, translation exceptions, and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.

Availability and Implementation

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

ANItools web: a web tool for fast genome comparison within multiple bacterial strains

Jit — Wed, 14 Nov 2018 04:34:23 -0600

ANItools is a software package written by PERL scripts that can be run in a Linux/Unix system. If you want to compare bacterial genomes and calculate their average nucleotide identity (ANI), you could download and run this program directly. Or you could send us the genome sequence by email. Then we will do the analysis work for you.

https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454

Address of the bookmark: http://ani.mypathogen.cn/

CANU: Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing.

Jit — Tue, 26 Apr 2016 11:38:10 -0500

Canu is a fork of the Celera Assembler designed for high-noise single-molecule sequencing (such as the PacBio RSII or Oxford Nanopore MinION). The software is currently alpha level, feel free to use and report issues encountered.

Canu is a hierachical assembly pipeline which runs in four steps:

Detect overlaps in high-noise sequences using MHAP
Generate corrected sequence consensus
Trim corrected sequences
Assemble trimmed corrected sequences

Read the documentation

New release https://github.com/marbl/canu/releases

Address of the bookmark: https://github.com/marbl/canu

PBSuite: Software for Long-Read Sequencing Data from PacBio

Jit — Mon, 27 Feb 2017 09:54:47 -0600

PBJelly - the genome upgrading tool.
PBHoney - the structural variation discovery tool

Both are contained within the PBSuite code found in downloads.

----- PBJelly -----
Read The Paper
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0047768

PBJelly is a highly automated pipeline that aligns long sequencing reads (such as PacBio RS reads or long 454 reads in fasta format) to high-confidence draft assembles. PBJelly fills or reduces as many captured gaps as possible to produce upgraded draft genomes.

----- PBHoney -----
Read The Paper
http://www.biomedcentral.com/1471-2105/15/180/abstract

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Address of the bookmark: https://sourceforge.net/projects/pb-jelly/

Tools to access the quality of your assembled genome !

LEGE — Thu, 08 Aug 2024 23:31:18 -0500

FASTA VALIDATOR + SEQKIT RMDUP: FASTA validation
GENOMETOOLS GT GFF3VALIDATOR: GFF3 validation
ASSEMBLATHON STATS: Assembly statistics
GENOMETOOLS GT STAT: Annotation statistics
NCBI FCS ADAPTOR: Adaptor contamination pass/fail
NCBI FCS GX: Foreign organism contamination pass/fail
BUSCO: Gene-space completeness estimation
TIDK: Telomere repeat identification
LAI: Continuity of repetitive sequences
KRAKEN2: Taxonomy classification
HIC CONTACT MAP: Alignment and visualisation of HiC data
MUMMER → CIRCOS + DOTPLOT & MINIMAP2 → PLOTSR: Synteny analysis
MERQURY: K-mer completeness, consensus quality and phasing assessment

LAMSA: fast split read alignment with long approximate matches

Jit — Tue, 15 May 2018 04:44:42 -0500

LAMSA (Long Approximate Matches-based Split Aligner) is a novel split alignment approach with faster speed and good ability of handling SV events. It is well-suited to align long reads (over thousands of base-pairs). LAMSA takes takes the advantage of the rareness of SVs to implement a specifically designed two-step strategy. That is, LAMSA initially splits the read into relatively long fragments and co-linearly align them to solve the small variations or sequencing errors, and mitigate the effect of repeats. The alignments of the fragments are then used for implementing a sparse dynamic programming (SDP)-based split alignment approach to handle the large or non-co-linear variants. We benchmarked LAMSA with simulated and real datasets having various read lengths and sequencing error rates, the results demonstrate that it is substantially faster than the state-of-the-art long read aligners; mean-while, it also has good ability to handle various categories of SVs. LAMSA is open source and free for non-commercial use. LAMSA is mainly designed by Bo Liu & Yan Gao and developed by Yan Gao in Center for Bioinformatics, Harbin Institute of Technology, China.

Address of the bookmark: https://github.com/hitbc/LAMSA

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

Jit — Mon, 22 May 2017 05:43:51 -0500

This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at kklam@eecs.berkeley.edu. My name is Ka-Kit Lam.

https://github.com/kakitone/MetaFinisherSC

https://github.com/kakitone/BIGMAC

Address of the bookmark: https://github.com/kakitone/BIGMAC

LRCstats: Long Read Correction Statistics

Jit — Fri, 05 Jan 2018 04:04:20 -0600

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats