Address of the bookmark: https://github.com/CMU-SAFARI/AirLift

Check out the detail at https://www.ncbi.nlm.nih.gov/nuccore/MN908947

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi

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.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

Address of the bookmark: https://www.ncbi.nlm.nih.gov/nuccore/ON563414

You can find the detail of reconstructed data at http://bioinfo.konkuk.ac.kr/DESCHRAMBLER/

Address of the bookmark: https://github.com/jkimlab/DESCHRAMBLER

Further information about fragScaff, including source code, is available at:https://sourceforge.net/projects/fragscaff/files.

Manuscript describing fragScaff was published as: Adey A, Kitzman JO, Burton JN, Daza R, Kumar A, Christiansen L, Ronaghi M, Amini S, L Gunderson K, Steemers FJ, Shendure J#. In vitro, long-range sequence information for de novo genome assembly via transposase contiguity. Genome Research 2014 Dec;24(12):2041-9. doi: 10.1101/gr.178319.114. PubMed PMID: 25327137.

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

Following are the list of Ancestral /sequence/ reconstruction (ASR) tools: 

inferCars

Reconstructs contiguous regions of an ancestral genome. Given information about adjacencies between conserved segments in each modern species, our goal is to infer segment order in the ancestral genome. To get a clean and precise statement of the problem, we formalize it using graph theory. We develop an algorithm that identifies a most parsimonious scenario for the history of each individual adjacency, although the whole-genome prediction is not guaranteed to optimize traditional measures like the number of breakpoints. We introduce weights to the graph edges to model the reliability of each adjacency.

ANGES:reconstructing ANcestral GEnomeS maps

A suite of Python programs that allows reconstructing ancestral genome maps from the comparison of the organization of extant-related genomes. ANGES can reconstruct ancestral genome maps for multichromosomal linear genomes and unichromosomal circular genomes. It implements methods inspired from techniques developed to compute physical maps of extant genomes.

Cocos

Constructs phylogenies of multi-domain proteins. With a given species tree and domain phylogenies, the procedure infers the composition of ancestral multi-domain proteins. Cocos implements and extend a suggested algorithmic approach by Behzadi and Vingron in an easy-to-use program. Such method could be applied to reconstruction of partial homologous units such as bacterial operons or protein complexes.

MySSP

Constructs an initial DNA sequence at the root of the tree and simulates evolution across the tree using a variety of common models of DNA evolution. MySSP is a program for the simulation of DNA sequence evolution across a phylogenetic tree. It is designed for large-scale studies, including simulation of multiple replicates and outputs sequences into NEXUS, MEGA, or FASTA formats. MySSP has a fairly simple graphical user interface (GUI) for basic use, but also has a specialized batch script interpreter to allow for more complicated or large-scale simulations.

PARANA: Parsimonious Ancestral Reconstruction And Network Analysis

Performs parsimony based inference of ancestral biological networks. Given multiple extant networks and phylogenetic information relating extant nodes, PARANA finds a parsimonious set of ancestral interaction events (edge gains and losses) which explain the extant networks. The framework adopted by PARANA is able to represent network evolution under models that support gene duplication and loss and independent interaction gain and loss. The method works on both directed and undirected networks and can incorporate asymmetric interaction gain and loss costs. In contrast to previous approaches, PARANA does not require knowing the relative ordering of unrelated duplication events and thus, works on phylogenetic trees even where branch lengths are not provided.

GapAdj: Gapped Adjacencies

A synteny-based method that is flexible enough to handle a model of evolution involving whole genome duplication events, in addition to rearrangements, gene insertions, and losses. Ancestral relationships between markers are defined in term of Gapped Adjacencies, i.e. pairs of markers separated by up to a given number of markers. It improves on a previous restricted to direct adjacencies, which revealed a high accuracy for adjacency prediction, but with the drawback of being overly conservative, i.e. of generating a large number of contiguous ancestral regions (CARs).

ANCESTOR

A web server allowing one to easily and quickly perform the last three steps of the ancestral genome reconstruction procedure. Ancestors implements several alignment algorithms, an indel maximum likelihood solver and a context-dependent maximum likelihood substitution inference algorithm. The results presented by the server include the posterior probabilities for the last two steps of the ancestral genome reconstruction and the expected error rate of each ancestral base prediction.

ProCARs

Reconstructs ancestral gene orders as contiguous ancestral regions (CARs) with a progressive homology-based method. ProCARs runs from a phylogeny tree (without branch lengths needed) with a marked ancestor and a block file. This homology-based method is based on iteratively detecting and assembling ancestral adjacencies, while allowing some micro-rearrangements of synteny blocks at the extremities of the progressively assembled CARs. The method starts with a set of blocks as the initial set of CARs, and detects iteratively the potential ancestral adjacencies between extremities of CARs, while building up the CARs progressively by adding, at each step, new non-conflicting adjacencies that induce the less homoplasy phenomenon. The species tree is used, in some additional internal steps, to compute a score for the remaining conflicting adjacencies, and to detect other reliable adjacencies, in order to reach completely assembled ancestral genomes.

FastML

A user-friendly tool for the reconstruction of ancestral sequences. FastML implements various novel features that differentiate it from existing tools: (i) FastML uses an indel-coding method, in which each gap, possibly spanning multiples sites, is coded as binary data. FastML then reconstructs ancestral indel states assuming a continuous time Markov process. FastML provides the most likely ancestral sequences, integrating both indels and characters; (ii) FastML accounts for uncertainty in ancestral states: it provides not only the posterior probabilities for each character and indel at each sequence position, but also a sample of ancestral sequences from this posterior distribution, and a list of the k-most likely ancestral sequences; (iii) FastML implements a large array of evolutionary models, which makes it generic and applicable for nucleotide, protein and codon sequences; and (iv) a graphical representation of the results is provided, including, for example, a graphical logo of the inferred ancestral sequences.

maxAlike

Reconstructs a genomic sequence for a specific taxon based on sequence homologs in other species. The input is a multiple sequence alignment and a phylogenetic tree that also contains the target species. For this target species, the algorithm computes nucleotide probabilities at each sequence position. Consensus sequences are then reconstructed based on a certain confidence level.

MLGO: Maximum Likelihood for Gene Order Analysis

A web tool for the reconstruction of phylogeny and/or ancestral genomes from gene-order data. MLGO was designed for analysis of large-scale genomic changes including not only rearrangements but also gene insertions, deletions and duplications. MLGO can be used to infer a phylogeny from genome rearrangement and gene order data, and can also obtain an estimation of ancestral genomes, given an input tree. MLGO takes the advantage of binary encoding on gene-order data, supports a fairly general model of genomic evolution (rearrangements plus duplications, insertions, and losses of genomic regions), and successfully accommodates itself into the framework of maximized likelihood.

Image Reference : Wiki

Address of the bookmark: https://www.broadinstitute.org/partnerships/education/broade/best-practices-variant-calling-gatk-1

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/