http://bioinfo.ut.ee/FastGT/

https://github.com/bioinfo-ut/GenomeTester4/

Address of the bookmark: http://bioinfo.ut.ee/FastGT/

Address of the bookmark: https://github.com/Megagenomics/FastProNGS

Study of 10 tools on five datasets that such a comparative evaluation of these tools is hindered by two types of circularity: they arise due to (1) the same variants or (2) different variants from the same protein occurring both in the datasets used for training and for evaluation of these tools, which may lead to overly optimistic results. Comparative evaluations of predictors that do not address these types of circularity may erroneously conclude that circularity confounded tools are most accurate among all tools, and may even outperform optimized combinations of tools.

Following tools are useful for mis sense muation detection ... 

PolyPhen‐2 (PP2)
“Predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations”

MutationTaster‐2 (MT2)
“Evaluation of the disease‐causing potential of DNA sequence alterations”

MutationAssessor (MASS)
“Predicts the functional impact of amino acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms”

LRT
“Identify a subset of deleterious mutations that disrupt highly conserved amino acids within protein‐coding sequences, which are likely to be unconditionally deleterious”

SIFT
“Predicts whether an amino acid substitution affects protein function”

GERP++
“Identifies constrained elements in multiple alignments by quantifying substitution deficits. These deficits represent substitutions that would have occurred if the element were neutral DNA, but did not occur because the element has been under functional constraint. We refer to these deficits as “rejected substitutions.” Rejected substitutions are a natural measure of constraint that reflects the strength of past purifying selection on the element”

phyloP
“Compute conservation or acceleration P values based on an alignment and a model of neutral evolution”

FatHMM unweighted (FatHMM‐U)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants”

FatHMM weighted (FatHMM‐W)
Predicts “functional consequences of both coding variants, that is, nonsynonymous single‐nucleotide variants, and noncoding variants” and its weighting scheme attributes higher tolerance scores to SNVs in proteins, related proteins, or domains that already include a high fraction of pathogenic variantsh

Combined Annotation Dependent Depletion (CADD)
“CADD is a tool for scoring the deleteriousness of single‐nucleotide variants as well as insertion/deletions variants in the human genome”

Toolkit for Automated and Rapid DIscovery of Structural variants

Requirements

zlib (http://www.zlib.net)
mrfast (https://github.com/BilkentCompGen/mrfast)
htslib (included as submodule; http://htslib.org/)
Fetching tardis

git clone https://github.com/BilkentCompGen/tardis.git --recursive

https://github.com/BilkentCompGen/tardis

Address of the bookmark: https://github.com/BilkentCompGen/tardis

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.

The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.

Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.

It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.

Address of the bookmark: http://mgra.cblab.org/

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Address of the bookmark: http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl

Address of the bookmark: https://github.com/reubwn/hgt

Address of the bookmark: https://sourceforge.net/projects/ksnp/files/

Following are the genome assembly tools based on string graph:

1.SGA (String Graph Assembler) https://github.com/jts/sga

Assembles large genomes from high coverage short read data. SGA is designed as a modular set of programs, which are used to form an assembly pipeline. SGA implements a set of assembly algorithms based on the FM-index. As the FM-index is a compressed data structure, the algorithms are very memory efficient. The SGA assembly has three distinct phases. The first phase corrects base calling errors in the reads. The second phase assembles contigs from the corrected reads. The third phase uses paired end and/or mate pair data to build scaffolds from the contigs. The output of this software is a PDF report that allows the properties of the genome and data quality to be visually explored. By providing more information to the user at the start of an assembly project, this software will help increase awareness of the factors that make a given assembly easy or difficult, assist in the selection of software and parameters and help to troubleshoot an assembly if it runs into problems.

2. SAGE: String-overlap Assembly of GEnomes https://github.com/lucian-ilie/SAGE2

SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.

3. FSG: Fast String Graph

The new integrated assembler has been assessed on a standard benchmark, showing that fast string graph (FSG) is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads. Moreover, we have studied the effect of coverage rates on the running times.

4.  BASE https://github.com/dhlbh/BASE

It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs. BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

5. Fermi https://github.com/lh3/fermi/

Fermi is a de novo assembler with a particular focus on assembling Illumina short sequence reads from a mammal-sized genome. In addition to the role of a typical assembler, fermi also aims to preserve heterozygotes which are often collapsed by other assemblers. Its ultimate goal is to find a minimal set of unitigs to represent all the information in raw reads.

If you want to learn about String Graph assembler, please read the following papers -

i) The Fragment Assembly String Graph - E. W. Myers

This paper describes the String Graph concept.

ii) Efficient construction of an assembly string graph using the FM-index - Jared T. Simpson and Richard Durbin

This earlier paper from Simpson and Durbin

iii) Efficient de novo assembly of large genomes using compressed data structures - Jared T. Simpson and Richard Durbin