Genome Annotation Information Research Articles

Complete genome analysis reveals environmental adaptability of sulfur-oxidizing bacterium Thioclava nitratireducens M1-LQ-LJL-11 and symbiotic relationship with deep-sea hydrothermal vent Chrysomallon squamiferum.

One sulfur-oxidizing bacterium Thioclava sp. M1-LQ-LJL-11 was isolated from the gill of Chrysomallon squamiferum collected from 2700m deep hydrothermal named Longqi on the southwest Indian Ocean ridge. In order to understand its survival mechanism in hydrothermal extreme environment and symbiotic relationship with its host, the complete genome of strain M1-LQ-LJL-11 was sequenced and analyzed. A total of 6117Mb of valid data was obtained, including 4096 coding genes, 61 non coding genes, including 9 rRNAs (among them, there are 3 in 23S rRNA, 3 in 5S rRNA, and 3 in 16S rRNA.), 52 tRNAs and 35 genomic islands. Strain M1-LQ-LJL-11 contains one chromosome and two plasmids. In the genome annotation information of the strain, we found 28 genes including cys sox, sor, sqr, tst related to sulfur metabolism and 17 metal resistance genes. Interestingly, a pair of quorum sensing system which probably regulating biofilm formation located in chromosome was found. These genes are critical for self-adaptation against severe environment as well as host survival. This study provides a basis understanding for the adaptive strategies of deep-sea hydrothermal bacteria and symbiotic relationship with its host in extreme environments through gene level.

Manual correction of genome annotation improved alternative splicing identification of Artemisia annua.

Gene annotation is essential for genome-based studies. However, algorithm-based genome annotation is difficult to fully and correctly reveal genomic information, especially for species with complex genomes. Artemisia annua L. is the only commercial resource of artemisinin production though the content of artemisinin is still to be improved. Genome-based genetic modification and breeding are useful strategies to boost artemisinin content and therefore, ensure the supply of artemisinin and reduce costs, but better gene annotation is urgently needed. In this study, we manually corrected the newly released genome annotation of A. annua using second- and third-generation transcriptome data. We found that incorrect gene information may lead to differences in structural, functional, and expression levels compared to the original expectations. We also identified alternative splicing events and found that genome annotation information impacted identifying alternative splicing genes. We further demonstrated that genome annotation information and alternative splicing could affect gene expression estimation and gene function prediction. Finally, we provided a valuable version of A. annua genome annotation and demonstrated the importance of gene annotation in future research.

FWAlgaeDB, an integrated genome database of freshwater algae

Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae.

Genome-Wide Identification and Expression Analysis of the HSF Gene Family in Poplar

The Heat Shock Factor (HSF) transcription factor family plays crucial roles in plant growth and development, as well as in protecting against adverse stresses. However, studies on the functions and regulatory mechanisms of the HSF genes are limited in poplar. Here, we identified and classified 30 HSF transcription factors in Populus trichocarpa based on recent genomic data and annotation information and conducted a comprehensive analysis of these proteins, including phylogenetic and physicochemical properties analysis, domain characterization, subcellular localization prediction, cis-acting elements analysis, sequence structure analysis, and chromosomal distribution. Our analysis revealed that segmental duplication events may be the main driving force behind the expansion of the poplar HSF gene family, and we explored the collinearity between poplar HSF genes and those of six other representative species. We also analyzed the tissue-specific and hormonal responses of the HSF genes in poplar and conducted gene co-expression network analysis, which revealed important molecular functions and biological processes related to growth and development, biotic and abiotic stress response, and epigenetic modification. These results provide significant insights into the functions and regulatory mechanisms of the HSF genes in poplar.

Haplotype analysis of genomic prediction by incorporating genomic pathway information based on high-density SNP marker in Chinese yellow-feathered chicken

Genomic selection using single nucleotide polymorphism (SNP) markers is now intensively investigated in breeding and has been widely utilized for genetic improvement. Currently, several studies have used haplotype (consisting of multiallelic SNPs) for genomic prediction and revealed its performance advantage. In this study, we comprehensively evaluated the performance of haplotype models for genomic prediction in 15 traits, including 6 growth, 5 carcass, and 4 feeding traits in a Chinese yellow-feathered chicken population. We adopted 3 methods to define haplotypes from high-density SNP panels, and our strategy included combining Kyoto Encyclopedia of Genes and Genomes pathway information and considering linkage disequilibrium (LD) information. Our results showed an increase in prediction accuracy due to haplotypes ranging from -0.04∼27.16% in all traits, where the significant improvements were found in 12 traits. The estimates of haplotype epistasis heritability were strongly correlated with the accuracy increase by haplotype models. In addition, incorporating genomic annotation information could further increase the accuracy of the haplotype model, where the further increase in accuracy is significantly relative to the increase of relative haplotype epistasis heritability. The genomic prediction using LD information for constructing haplotypes has the best prediction performance among the 4 traits. These results uncovered that haplotype methods were beneficial for genomic prediction, and the accuracy could be further increased by incorporating genomic annotation information. Moreover, using LD information would potentially improve the performance of genomic prediction.

PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness

The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.

IAnimal: a cross-species omics knowledgebase for animals.

With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq)and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.

Saccharomyces cerevisiae cis-acting DNA sequences curation pipeline (Sc-cADSs-CP): Master transcription factors prediction in yeasts

Designing promoter architectures hinges on genomic and functional annotation. Saccharomyces cerevisiae is the first model yeast whose databases host genomic and functional annotation information. To predict transcription factors (TFs) regulating central pathways in yeasts, we first introduce S. cerevisiae cis-acting DNA sequences/sites (cADSs) curation pipeline (Sc-cADSs-CP). The promoters of the genes involved in the central pathways of S. cerevisiae were retrieved from the genome sequences. We processed the binding frequency matrices of TFs with the following two criteria. First, we extracted cADSs based on the TF motifs in the TRANSFAC database; then, if there were more than one frequency matrix for a TF, the longest one with the maximum sensitivity was used. Next, we developed the direct scanning algorithm ScanAlgo (uses the Biopython-library), scanning DNA motifs for pairing cross-species alignments. We used the tools Sc-cADSs-CP and ScanAlgo to predict master TFs for Pichia pastoris, which lacks extensive functional annotation studies. The phylogenetic footprinting results were obtained by aligning the scanned S. cerevisiae promoters against orthologous P. pastoris promoters. The predicted cADSs were summed into position weight matrices unique to P. pastoris. We annotated 116 TFs based on the phylogenetic footprinting predictions of cADSs regulating the central pathways in P. pastoris. The presented methodology with the tools Sc-cADSs-CP and ScanAlgo enables the prediction of master TFs and cADSs in yeasts.

A chromosome-level genome assembly of Artocarpus nanchuanensis (Moraceae), an extremely endangered fruit tree.

Artocarpus nanchuanensis (Moraceae), which is naturally distributed in China, is a representative and extremely endangered tree species. In this study, we obtained a high-quality chromosome-scale genome assembly and annotation information for A. nanchuanensis using integrated approaches, including Illumina, Nanopore sequencing platform, and Hi-C. A total of 128.71 Gb of raw Nanopore reads were generated from 20-kb libraries, and 123.38 Gb of clean reads were obtained after filtration with 160.34× coverage depth and a 17.48-kb average read length. The final assembled A. nanchuanensis genome was 769.44 Mb with a 2.09 Mb contig N50, and 99.62% (766.50 Mb) of the assembled data was assigned to 28 pseudochromosomes.In total, 39,596 genes (95.10%, 39,596/41,636) were successfully annotated, and 129 metabolic pathways were detected. Plants disease resistance/insect resistance genes, plant–pathogen interaction metabolic pathways, and abundant biosynthesis pathways of vitamins, flavonoid, and gingerol were detected. Unigene reveals the basis of species-specific functions, and gene family in contraction and expansion generally implies strong functional differences in the evolution. Compared with other related species, a total of 512 unigenes, 309 gene families in contraction, and 559 gene families in expansion were detected in A. nanchuanensis.This A. nanchuanensis genome information provides an important resource to expand our understanding of the unique biological processes, nutritional and medicinal benefits, and evolutionary relationship of this species. The study of gene function and metabolic pathway in A. nanchuanensis may reveal the theoretical basis of a special trait in A. nanchuanensis and promote the study and utilization of its rare medicinal value.

Comparative analysis of genome-based CAZyme cassette in Antarctic Microbacterium sp. PAMC28756 with 31 other Microbacterium species.

The genus Microbacterium belongs to the family Microbacteriaceae and phylum Actinobacteria. A detailed study on the complete genome and systematic comparative analysis of carbohydrate-active enzyme (CAZyme) among the Microbacterium species would add knowledge on metabolic and environmental adaptation. Here we present the comparative genomic analysis of CAZyme using the complete genome of Antarctic Microbacterium sp. PAMC28756 with other complete genomes of 31 Microbacterium species available. The genomic and CAZyme comparison of Microbacterium species and to rule out the specific features of CAZyme for the environmental and metabolic adaptation. Bacterial source were collected from NCBI database, CAZyme annotation of Microbacterium species was analyzed using dbCAN2 Meta server. Cluster of orthologous groups (COGs) analysis was performed using the eggNOG4.5 database. Whereas, KEGG database was used to compare and obtained the functional genome annotation information in carbohydrate metabolism and glyoxylate cycle. Out of 32 complete genomes of Microbacterium species, strain No. 7 isolated from Activated Sludge showed the largest genomic size at 4.83Mb. The genomic size of PAMC28756 isolated from Antarctic lichen species Stereocaulons was 3.54Mb, the G + C content was 70.4% with 3,407 predicted genes, of which 3.36% were predicted CAZyme. In addition, while comparing the Glyoxylate cycle among 32 bacteria, except 10 strains, all other, including our strain have Glyoxylate pathway. PAMC28756 contained the genes that degrade cellulose, hemicellulose, amylase, pectinase, chitins and other exo-and endo glycosidases. Utilizing these polysaccharides can provides source of energy in an extreme environment. In addition, PAMC28756 assigned the (10.15%) genes in the carbohydrate transport and metabolism functional group closely related to the CAZyme for polysaccharides degradation. The genomic content and CAZymes distribution was varied in Microbacterium species. There was the presence of more than 10% genes in the carbohydrate transport and metabolism functional group closely related to the CAZyme for polysaccharides degradation. In addition, occurrence of glyoxylate cycle for alternative utilization of carbon sources suggest the adaptation of PAMC28756 in the harsh microenvironment.

Incorporating genomic annotation into single-step genomic prediction with imputed whole-genome sequence data

Single-step genomic best linear unbiased prediction (ssGBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence (WGS) data at the population level, more attention is being paid to the usage of WGS data in ssGBLUP. The predictive ability of ssGBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ssGBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms (SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ssGBLUP, haplotype-based ssGHBLUP, and four extended ssGBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation (GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ssGBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ssGBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ssGBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family (SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ssGBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ssGBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ssGBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.

A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates

BackgroundLipomyces starkeyi has been widely regarded as a promising oleaginous yeast with broad industrial application prospects because of its wide substrate spectrum, good adaption to fermentation inhibitors, excellent fatty acid composition for high-quality biodiesel, and negligible lipid remobilization. However, the currently low experimental lipid yield of L. starkeyi prohibits its commercial success. Metabolic model is extremely valuable to comprehend the complex biochemical processes and provide great guidance for strain modification to facilitate the lipid biosynthesis.ResultsA small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annotation information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the flux balance analysis (FBA). The optimal flux distribution of the lipid synthesis showed that pentose phosphate pathway (PPP) independently met the necessity of NADPH for lipid synthesis, resulting in the relatively low lipid yields. Several targets (NADP-dependent oxidoreductases) beneficial for oleaginicity of L. starkeyi with significantly higher theoretical lipid yields were compared and elucidated. The combined utilization of acetic acid and other carbon sources and a hypothetical reverse β-oxidation (RBO) pathway showed outstanding potential for improving the theoretical lipid yield.ConclusionsThe lipid biosynthesis potential of L. starkeyi can be significantly improved through appropriate modification of metabolic network, as well as combined utilization of carbon sources according to the metabolic model. The prediction and analysis provide valuable guidance to improve lipid production from various low-cost substrates.

Comparative Genomics of Mycoplasma synoviae and New Targets for Molecular Diagnostics.

Mycoplasma synoviae is an important pathogen of poultry, causing significant economic losses in this industry. Analysis of the unique genes and shared genes among different M. synoviae strains and among related species is helpful for studying the molecular pathogenesis of M. synoviae and provides valuable molecular diagnostic targets to facilitate the identification of M. synoviae species. We selected a total of 46 strains, including six M. synoviae strains, from 25 major animal (including avian) Mycoplasma species/subspecies that had complete genome sequences and annotation information published in GenBank, and used them for comparative genomic analysis. After analysis, 16 common genes were found in the 46 strains. Thirteen single-copy core genes and the 16s rRNA genes were used for genetic evolutionary analysis. M. synoviae was found to have a distant evolutionary relationship not only with other arthritis-causing mycoplasmas, but also with another major avian pathogen, Mycoplasma gallisepticum, that shares the major virulence factor vlhA with M. synoviae. Subsequently, six unique coding genes were identified as shared among these M. synoviae strains that are absent in other species with published genome sequences. Two of the genes were found to be located in the genetically stable regions of the genomes of M. synoviae and were determined to be present in all M. synoviae isolated strains (n = 20) and M. synoviae-positive clinical samples (n = 48) preserved in our laboratory. These two genes were used as molecular diagnostic targets for which SYBR green quantitative PCR detection methods were designed. The two quantitative PCR methods exhibited good reproducibility and high specificity when tested on positive plasmid controls and genomic DNA extracted from different M. synoviae strains, other major avian pathogenic bacteria/mycoplasmas, and low pathogenic Mycoplasma species. The detection limit for the two genes was 10 copies or less per reaction. The clinical sensitivity and specificity of the quantitative PCR methods were both 100% based on testing chicken hock joint samples with positive or negative M. synoviae infection. This research provides a foundation for the study of species-specific differences and molecular diagnosis of M. synoviae.

DbPSP 2.0, an updated database of protein phosphorylation sites in prokaryotes

In prokaryotes, protein phosphorylation plays a critical role in regulating a broad spectrum of biological processes and occurs mainly on various amino acids, including serine (S), threonine (T), tyrosine (Y), arginine (R), aspartic acid (D), histidine (H) and cysteine (C) residues of protein substrates. Through literature curation and public database integration, here we reported an updated database of phosphorylation sites (p-sites) in prokaryotes (dbPSP 2.0) that contains 19,296 experimentally identified p-sites in 8,586 proteins from 200 prokaryotic organisms, which belong to 12 phyla of two kingdoms, bacteria and archaea. To carefully annotate these phosphoproteins and p-sites, we integrated the knowledge from 88 publicly available resources that covers 9 aspects, namely, taxonomy annotation, genome annotation, function annotation, transcriptional regulation, sequence and structure information, family and domain annotation, interaction, orthologous information and biological pathway. In contrast to version 1.0 (~30 MB), dbPSP 2.0 contains ~9 GB of data, with a 300-fold increased volume. We anticipate that dbPSP 2.0 can serve as a useful data resource for further investigating phosphorylation events in prokaryotes. dbPSP 2.0 is free for all users to access at: http://dbpsp.biocuckoo.cn.

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.

Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3′/5′ untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4–9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.

DDBJ Data Analysis Challenge: a machine learning competition to predict <i>Arabidopsis</i> chromatin feature annotations from DNA sequences

Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with knowledge of machine learning techniques is difficult for many experimental life scientists. One solution to this problem is to utilise the power of crowdsourcing. In this report, we describe how we investigated the potential of crowdsourced modelling for a life science task by conducting a machine learning competition, the DNA Data Bank of Japan (DDBJ) Data Analysis Challenge. In the challenge, participants predicted chromatin feature annotations from DNA sequences with competing models. The challenge engaged 38 participants, with a cumulative total of 360 model submissions. The performance of the top model resulted in an area under the curve (AUC) score of 0.95. Over the course of the competition, the overall performance of the submitted models improved by an AUC score of 0.30 from the first submitted model. Furthermore, the 1st- and 2nd-ranking models utilised external data such as genomic location and gene annotation information with specific domain knowledge. The effect of incorporating this domain knowledge led to improvements of approximately 5%-9%, as measured by the AUC scores. This report suggests that machine learning competitions will lead to the development of highly accurate machine learning models for use by experimental scientists unfamiliar with the complexities of data science.

Rice Stress-Resistant SNP Database

BackgroundRice (Oryza sativa L.) yield is limited inherently by environmental stresses, including biotic and abiotic stresses. Thus, it is of great importance to perform in-depth explorations on the genes that are closely associated with the stress-resistant traits in rice. The existing rice SNP databases have made considerable contributions to rice genomic variation information but none of them have a particular focus on integrating stress-resistant variation and related phenotype data into one web resource.ResultsRice Stress-Resistant SNP database (http://bioinformatics.fafu.edu.cn/RSRS) mainly focuses on SNPs specific to biotic and abiotic stress-resistant ability in rice, and presents them in a unified web resource platform. The Rice Stress-Resistant SNP (RSRS) database contains over 9.5 million stress-resistant SNPs and 797 stress-resistant candidate genes in rice, which were detected from more than 400 stress-resistant rice varieties. We incorporated the SNPs function, genome annotation and phenotype information into this database. Besides, the database has a user-friendly web interface for users to query, browse and visualize a specific SNP efficiently. RSRS database allows users to query the SNP information and their relevant annotations for individual variety or more varieties. The search results can be visualized graphically in a genome browser or displayed in formatted tables. Users can also align SNPs between two or more rice accessions.ConclusionRSRS database shows great utility for scientists to further characterize the function of variants related to environmental stress-resistant ability in rice.

TASUKE+: a web-based platform for exploring genome-wide association studies results and large-scale resequencing data.

Recent revolutionary advancements in sequencing technologies have made it possible to obtain mass quantities of genome-scale sequence data in a cost-effective manner and have drastically altered molecular biological studies. To utilize these sequence data, genome-wide association studies (GWASs) have become increasingly important. Hence, there is an urgent need to develop a visualization tool that enables efficient data retrieval, integration of GWAS results with diverse information and rapid public release of such large-scale genotypic and phenotypic data. We developed a web-based genome browser TASUKE+ (https://tasuke.dna.affrc.go.jp/), which is equipped with the following functions: (i) interactive GWAS results visualization with genome resequencing data and annotation information, (ii) PCR primer design, (iii) phylogenetic tree reconstruction and (iv) data sharing via the web. GWAS results can be displayed in parallel with polymorphism data, read depths and annotation information in an interactive and scalable manner. Users can design PCR primers for polymorphic sites of interest. In addition, a molecular phylogenetic tree of any region can be reconstructed so that the overall relationship among the examined genomes can be understood intuitively at a glance. All functions are implemented through user-friendly web-based interfaces so that researchers can easily share data with collaborators in remote places without extensive bioinformatics knowledge.

IVikodak-A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities.

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/.

Chromosomal level assembly and population sequencing of the Chinese tree shrew genome.

Chinese tree shrews (Tupaia belangeri chinensis) have become an increasingly important experimental animal in biomedical research due to their close relationship to primates. An accurately sequenced and assembled genome is essential for understanding the genetic features and biology of this animal. In this study, we used long-read single-molecule sequencing and high-throughput chromosome conformation capture (Hi-C) technology to obtain a high-qualitychromosome-scale scaffolding of the Chinese tree shrew genome. The new reference genome (KIZ version 2: TS_2.0) resolved problems in presently available tree shrew genomes and enabled accurate identification of large and complex repeat regions, gene structures, and species-specific genomic structural variants. In addition, by sequencing the genomes of six Chinese tree shrew individuals, we produced a comprehensive map of 12.8 M single nucleotide polymorphisms and confirmed that the major histocompatibility complex (MHC) loci and immunoglobulin gene family exhibited high nucleotide diversity in the tree shrew genome. We updated the tree shrew genome database (TreeshrewDB v2.0: http://www.treeshrewdb.org) to include the genome annotation information and genetic variations. The new high-quality reference genome of the Chinese tree shrew and the updated TreeshrewDB will facilitate the use of this animal in many different fields of research.