Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

The data are available using NCBI accession IDs: BioProject: (PRJNA483067), assembly: [RBJD00000000] and sequence data (SRP155659).

Additional Resources

• Interactive map showcasing global initiatives underway to generate reference-quality human genome assemblies for diverse populations
• BioReport Podcast on the value of ethnic-specific reference genomes
• Nature Reviews Genetics paper from NHGRI: Prioritizing diversity in human genomics research
• Article in The Journal of Precision Medicine: “Minority Report – Ethnic Diversity and the Real Promise for Precision Medicine”
• Article in Bio-IT World: “Genomic Data Standards Are a Necessity”
• NHGRI Project Award: High Quality Human and Non-Human Primate Genome Assemblies

More details are available on the PacBio website:

• Blog post: Data Release: Highest-Quality, Most Contiguous Individual Human Genome Assembly to Date
• Blog post: For Reference-Grade Human Genome Assemblies, SMRT Sequencing Yields Optimal Results
• Webinar:  Assembling High-Quality Human Reference Genomes for Global Populations
• FALCON-Phase press release and article preprint
• PacBio research focus webpage about Human Population Genetics

 Ref: https://stockguru.com/2018/10/08/pacific-biosciences-releases-highest-quality-most-contiguous-individual-human-genome-assembly-to-date/

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

More http://www.nieduszynski.org/index.php
http://www.path.ox.ac.uk/research/cell-biology-and-pathology/conrad-nieduszynski-group

http://ggdc.dsmz.de/ggdc_background.php#

Genome-to-Genome Distance Calculator 2.1

http://ggdc.dsmz.de/ggdc.php

Address of the bookmark: http://ggdc.dsmz.de/

Experience Required: 3-5 years of experience

No of Positions : Multiple

Qualifications: Candidates with minimum qualification as M.Sc Bioinformatics with 3-5 years of experience in Life sciences R&D or Pharma Industry.

Ph.D candidates with research experience in Bioinformatics with publications in International journal and minimum 2 years of industry experience in clinical genomics will be preferred for this position.

Requirement:

1. Must have basic understanding of molecular biology and Genomics.

2. Experience in application development or must have expertise in programming using either of Perl/Python.

3. Experience in statistical programming using R/Bioconductor/Matlab.

4. Strong concept in statistical and mathematical modelling.

5. Experience in designing and developing the bioinformatics pipeline.

6. Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.

7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.

8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.

9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.

10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.

11. Ability to work as team as well as independently with minimal support.

More at http://www.ocimumbio.com/careers1/

More at http://bioinfo.vanderbilt.edu/zhanglab/

Transcription is the process by which the information in DNA is copied into messenger RNA (mRNA) for protein production. Originally created for DNA Interactive ( http://www.dnai.org ). TRANSCRIPT: The Central Dogma of Molecular Biology: "DNA makes RNA makes protein" Here the process begins. Transcription factors assemble at a specific promoter region along the DNA. The length of DNA following the promoter is a gene and it contains the recipe for a protein. A mediator protein complex arrives carrying the enzyme RNA polymerase. It manoeuvres the RNA polymerase into place... inserting it with the help of other factors between the strands of the DNA double helix. The assembled collection of all these factors is referred to as the transcription initiation complex... and now it is ready to be activated. The initiation complex requires contact with activator proteins, which bind to specific sequences of DNA known as enhancer regions. These regions may be thousands of base pairs distant from the start of the gene. Contact between the activator proteins and the initiation-complex releases the copying mechanism. The RNA polymerase unzips a small portion of the DNA helix exposing the bases on each strand. Only one of the strands is copied. It acts as a template for the synthesis of an RNA molecule which is assembled one sub-unit at a time by matching the DNA letter code on the template strand. The sub-units can be seen here entering the enzyme through its intake hole and they are joined together to form the long messenger RNA chain snaking out of the top.

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.

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