BOL: Related items

ACANA: An accurate and consistent alignment tool for DNA sequences

Jit — Wed, 06 Dec 2017 09:45:29 -0600

ACANA is an accurate and consistent alignment tool for DNA sequences. ACANA is specifically designed for aligning sequences that share only some moderately conserved regions and/or have a high frequency of long insertions or deletions. It attempts to combine the best of local and global alignments algorithms in searching for evolutionarily related regions of sequences in order to achieve the best alignment. ACANA is also robust to the small changes of alignment parameters, particularly the gap extension score. As an accurate alignment tool, ACANA is particularly useful in comparative sequence analysis for identifying conserved functional regulatory elements.

Address of the bookmark: https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm

P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads

Jit — Tue, 12 Jun 2018 08:14:41 -0500

P_RNA_scaffolder, a fast and accurate tool using paired-end RNA-sequencing reads to scaffold genomes. This tool aims to improve the completeness of both protein-coding and non-coding genes. After this tool was applied to scaffolding human contigs, the structures of both protein-coding genes and circular RNAs were almost completely recovered and equivalent to those in a complete genome, especially for long proteins and long circular RNAs.

Address of the bookmark: http://www.fishbrowser.org/software/P_RNA_scaffolder/

MSAProbs - Parallel and accurate multiple sequence alignment

Neel — Tue, 09 Jul 2019 23:58:44 -0500

MSAProbs is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.

Address of the bookmark: http://msaprobs.sourceforge.net/homepage.htm#latest

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

http://clark.cs.ucr.edu/Spaced/

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/

Liftoff: An accurate GFF3/GTF lift over pipeline

Neel — Sun, 20 Dec 2020 01:36:37 -0600

Liftoff is a tool that accurately 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

HELIANO: A fast and accurate tool for detection of Helitron-like elements

LEGE — Tue, 13 Aug 2024 07:16:34 -0500

Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.

https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true

Address of the bookmark: https://github.com/Zhenlisme/heliano/

OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

Jit — Tue, 14 Nov 2017 09:30:35 -0600

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.

http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S1672022917301663

JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges

Shruti Paniwala — Sat, 02 Jul 2022 11:41:53 -0500

This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.

Address of the bookmark: https://github.com/mkirsche/Jasmine

Unicycler: Hybrid assembly pipeline for bacterial genomes

Jit — Fri, 10 Nov 2017 03:58:27 -0600

Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Address of the bookmark: https://github.com/rrwick/Unicycler

SPAdes hybrid genome assembly

Jit — Mon, 27 Nov 2017 08:05:40 -0600

When you have both Illumina and Nanopore data, then SPAdes remains a good option for hybrid assembly - SPAdes was used to produce the B fragilis assembly by Mick Watson’s group.

Again, running spades.py will show you the options:

spades.py

This produces:

SPAdes genome assembler v3.10.1

Usage: /usr/local/SPAdes-3.10.1-Linux/bin/spades.py [options] -o 

Basic options:
-o          directory to store all the resulting files (required)
--sc                    this flag is required for MDA (single-cell) data
--meta                  this flag is required for metagenomic sample data
--rna                   this flag is required for RNA-Seq data
--plasmid               runs plasmidSPAdes pipeline for plasmid detection
--iontorrent            this flag is required for IonTorrent data
--test                  runs SPAdes on toy dataset
-h/--help               prints this usage message
-v/--version            prints version

Input data:
--12          file with interlaced forward and reverse paired-end reads
-1            file with forward paired-end reads
-2            file with reverse paired-end reads
-s            file with unpaired reads
--pe<#>-12            file with interlaced reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-1             file with forward reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-2             file with reverse reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-s             file with unpaired reads for paired-end library number <#> (<#> = 1,2,..,9)
--pe<#>-    orientation of reads for paired-end library number <#> (<#> = 1,2,..,9;  = fr, rf, ff)
--s<#>                file with unpaired reads for single reads library number <#> (<#> = 1,2,..,9)
--mp<#>-12            file with interlaced reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-1             file with forward reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-2             file with reverse reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-s             file with unpaired reads for mate-pair library number <#> (<#> = 1,2,..,9)
--mp<#>-    orientation of reads for mate-pair library number <#> (<#> = 1,2,..,9;  = fr, rf, ff)
--hqmp<#>-12          file with interlaced reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-1           file with forward reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-2           file with reverse reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-s           file with unpaired reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9)
--hqmp<#>-  orientation of reads for high-quality mate-pair library number <#> (<#> = 1,2,..,9;  = fr, rf, ff)
--nxmate<#>-1         file with forward reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--nxmate<#>-2         file with reverse reads for Lucigen NxMate library number <#> (<#> = 1,2,..,9)
--sanger              file with Sanger reads
--pacbio              file with PacBio reads
--nanopore            file with Nanopore reads
--tslr        file with TSLR-contigs
--trusted-contigs             file with trusted contigs
--untrusted-contigs           file with untrusted contigs

Pipeline options:
--only-error-correction runs only read error correction (without assembling)
--only-assembler        runs only assembling (without read error correction)
--careful               tries to reduce number of mismatches and short indels
--continue              continue run from the last available check-point
--restart-from      restart run with updated options and from the specified check-point ('ec', 'as', 'k', 'mc')
--disable-gzip-output   forces error correction not to compress the corrected reads
--disable-rr            disables repeat resolution stage of assembling

Advanced options:
--dataset             file with dataset description in YAML format
-t/--threads               number of threads
                                [default: 16]
-m/--memory                RAM limit for SPAdes in Gb (terminates if exceeded)
                                [default: 250]
--tmp-dir              directory for temporary files
                                [default: /tmp]
-k                 comma-separated list of k-mer sizes (must be odd and
                                less than 128) [default: 'auto']
--cov-cutoff             coverage cutoff value (a positive float number, or 'auto', or 'off') [default: 'off']
--phred-offset  <33 or 64>      PHRED quality offset in the input reads (33 or 64)
                                [default: auto-detect]

As you can see this is also a “pipeline” of tools that can be switched on or off. SPAdes takes quite a long time, so for the purposes of this practical, something like this may suffice:

spades.py -t 4 \
          -m 32 \
          -k 31,51,71 \
          --only-assembler \
          -1 miseq.1.fastq -2 miseq.2.fastq \
          --nanopore minion.fastq \
          -o hybrid_assembly

In turn, these parameters mean

use 4 threads
max memory is 32Gb
use 3 kmer values to build the de bruijn graph(s) - 31, 51 and 71
only run the assembler, not the correction algorithm (for speed)
read 1 and read 2 of the MiSeq data
the nanopore data
put the output in folder “hybrid_assembly”