scholarly journals Special Issue: Genomic Analyses of Avian Evolution

Diversity ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 178
Author(s):  
Peter Houde
Keyword(s):  
Genome Organization ◽  
De Novo ◽  
State Of The Art ◽  
Phylogenetic Inference ◽  
Special Issue ◽  
Model Species ◽  
De Novo Genome Assembly ◽  
Avian Evolution ◽  
New Methods ◽  
Genomic Analyses

“Genomic Analyses of Avian Evolution” is a “state of the art” showcase of the varied and rapidly evolving fields of inquiry enabled and driven by powerful new methods of genome sequencing and assembly as they are applied to some of the world’s most familiar and charismatic organisms—birds. The contributions to this Special Issue are as eclectic as avian genomics itself, but loosely interrelated by common underpinnings of phylogenetic inference, de novo genome assembly of non-model species, and genome organization and content.

scholarly journals Genomic analyses unveil helmeted guinea fowl (Numida meleagris) domestication in West Africa

2021 ◽  
Author(s):  
Quan-Kuan Shen ◽  
Min-Sheng Peng ◽  
Adeniyi C Adeola ◽  
Ling Kui ◽  
Shengchang Duan ◽  
...  
Keyword(s):  
West Africa ◽  
De Novo ◽  
Guinea Fowl ◽  
Chromatin Interaction ◽  
De Novo Genome Assembly ◽  
Numida Meleagris ◽  
Whole Genomes ◽  
Genomic Analyses ◽  
Related Wild Species ◽  
Wild Progenitors

Abstract Domestication of the helmeted guinea fowl (HGF; Numida meleagris) in Africa remains elusive. Here we report a high-quality de novo genome assembly for domestic HGF generated by long and short-reads sequencing together with optical and chromatin interaction mapping. Using this assembly as the reference, we performed population genomic analyses for newly sequenced whole-genomes for 129 birds from Africa, Asia, and Europe, including domestic animals (n = 89), wild progenitors (n = 34), and their closely related wild species (n = 6). Our results reveal domestication of HGF in West Africa around 1,300-5,500 years ago. Scanning for selective signals characterized the functional genes in behavior and locomotion changes involved in domestication of HGF. The pleiotropy and linkage in genes affecting plumage color and fertility were revealed in the recent breeding of Italian domestic HGF. In addition to presenting a missing piece to the jigsaw puzzle of domestication in poultry, our study provides valuable genetic resources for researchers and breeders to improve production in this species.

scholarly journals Raven: a de novo genome assembler for long reads

2020 ◽  
Author(s):  
Robert Vaser ◽  
Mile Šikić
Keyword(s):  
Human Genome ◽  
Genome Assembly ◽  
De Novo ◽  
De Novo Genome Assembly ◽  
New Methods ◽  
Long Reads ◽  
Long Read ◽  
Comparable Accuracy ◽  
Genome Assembler ◽  
Genome Dataset

We present new methods for the improvement of long-read de novo genome assembly incorporated into a straightforward tool called Raven (https://github.com/lbcb-sci/raven). Compared with other assemblers, Raven is one of two fastest, it reconstructs the sequenced genome in the least amount of fragments, has better or comparable accuracy, and maintains similar performance for various genomes. Raven takes 500 CPU hours to assemble a 44x human genome dataset in only 259 fragments.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
De Novo ◽  
De Novo Drug Design ◽  
Generative Process ◽  
New Methods ◽  
Multiparameter Optimization ◽  
Long Short Term Memory ◽  
New Compounds

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>

scholarly journals HP1 drives de novo 3D genome reorganization in early Drosophila embryos

Nature ◽  
2021 ◽  
Author(s):  
Fides Zenk ◽  
Yinxiu Zhan ◽  
Pavel Kos ◽  
Eva Löser ◽  
Nazerke Atinbayeva ◽  
...  
Keyword(s):  
Genome Organization ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Early Embryo ◽  
Heterochromatin Protein ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Genome Reorganization

AbstractFundamental features of 3D genome organization are established de novo in the early embryo, including clustering of pericentromeric regions, the folding of chromosome arms and the segregation of chromosomes into active (A-) and inactive (B-) compartments. However, the molecular mechanisms that drive de novo organization remain unknown1,2. Here, by combining chromosome conformation capture (Hi-C), chromatin immunoprecipitation with high-throughput sequencing (ChIP–seq), 3D DNA fluorescence in situ hybridization (3D DNA FISH) and polymer simulations, we show that heterochromatin protein 1a (HP1a) is essential for de novo 3D genome organization during Drosophila early development. The binding of HP1a at pericentromeric heterochromatin is required to establish clustering of pericentromeric regions. Moreover, HP1a binding within chromosome arms is responsible for overall chromosome folding and has an important role in the formation of B-compartment regions. However, depletion of HP1a does not affect the A-compartment, which suggests that a different molecular mechanism segregates active chromosome regions. Our work identifies HP1a as an epigenetic regulator that is involved in establishing the global structure of the genome in the early embryo.

scholarly journals Annelid Diversity: Historical Overview and Future Perspectives

Diversity ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 129
Author(s):  
María Capa ◽  
Pat Hutchings
Keyword(s):  
State Of The Art ◽  
Molecular Taxonomy ◽  
Evolutionary Relationships ◽  
Diverse Group ◽  
Future Perspectives ◽  
Special Issue ◽  
Species Numbers ◽  
Species Complexes ◽  
Taxonomic Groups ◽  
A New Species

Annelida is a ubiquitous, common and diverse group of organisms, found in terrestrial, fresh waters and marine environments. Despite the large efforts put into resolving the evolutionary relationships of these and other Lophotrochozoa, and the delineation of the basal nodes within the group, these are still unanswered. Annelida holds an enormous diversity of forms and biological strategies alongside a large number of species, following Arthropoda, Mollusca, Vertebrata and perhaps Platyhelminthes, among the species most rich in phyla within Metazoa. The number of currently accepted annelid species changes rapidly when taxonomic groups are revised due to synonymies and descriptions of a new species. The group is also experiencing a recent increase in species numbers as a consequence of the use of molecular taxonomy methods, which allows the delineation of the entities within species complexes. This review aims at succinctly reviewing the state-of-the-art of annelid diversity and summarizing the main systematic revisions carried out in the group. Moreover, it should be considered as the introduction to the papers that form this Special Issue on Systematics and Biodiversity of Annelids.

Machine learning for rediscovering revolutionary ideas of the past

2021 ◽  
pp. 105971232098304
Author(s):  
R Alexander Bentley ◽  
Joshua Borycz ◽  
Simon Carrignon ◽  
Damian J Ruck ◽  
Michael J O’Brien
Keyword(s):  
Language Processing ◽  
Scientific Progress ◽  
Phylogenetic Inference ◽  
The Past ◽  
New Methods ◽  
Inherent Nature ◽  
Social Learning Processes ◽  
High Level ◽  
Scientific Ideas ◽  
Revolutionary Science

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

scholarly journals nanotatoR: a tool for enhanced annotation of genomic structural variants

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Surajit Bhattacharya ◽  
Hayk Barseghyan ◽  
Emmanuèle C. Délot ◽  
Eric Vilain
Keyword(s):  
De Novo ◽  
Genome Mapping ◽  
Gene List ◽  
Sufficient Information ◽  
Rna Seq ◽  
Structural Variants ◽  
De Novo Genome Assembly ◽  
Pathogenic Variants ◽  
Increased Sensitivity

Abstract Background Whole genome sequencing is effective at identification of small variants, but because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity. Results We developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient’s phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR’s annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM. Conclusions The extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

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