Bioinformatics Software Tools Research Articles

Pathway Tools Visualization of Organism-Scale Metabolic Networks.

Metabolomics, synthetic biology, and microbiome research demand information about organism-scale metabolic networks. The convergence of genome sequencing and computational inference of metabolic networks has enabled great progress toward satisfying that demand by generating metabolic reconstructions from the genomes of thousands of sequenced organisms. Visualization of whole metabolic networks is critical for aiding researchers in understanding, analyzing, and exploiting those reconstructions. We have developed bioinformatics software tools that automatically generate a full metabolic-network diagram for an organism, and that enable searching and analyses of the network. The software generates metabolic-network diagrams for unicellular organisms, for multi-cellular organisms, and for pan-genomes and organism communities. Search tools enable users to find genes, metabolites, enzymes, reactions, and pathways within a diagram. The diagrams are zoomable to enable researchers to study local neighborhoods in detail and to see the big picture. The diagrams also serve as tools for comparison of metabolic networks and for interpreting high-throughput datasets, including transcriptomics, metabolomics, and reaction fluxes computed by metabolic models. These data can be overlaid on the metabolic charts to produce animated zoomable displays of metabolic flux and metabolite abundance. The BioCyc.org website contains whole-network diagrams for more than 18,000 sequenced organisms. The ready availability of organism-specific metabolic network diagrams and associated tools for almost any sequenced organism are useful for researchers working to better understand the metabolism of their organism and to interpret high-throughput datasets in a metabolic context.

Bioinformatics: scope and challenges in aquaculture research of Bangladesh- a review

Bioinformatics is one of the ongoing trends of biological research integrating gene based information and computational technology to produce new knowledge. It works to synthesize complex biological information from multiomics data (results of high throughput technologies) by employing a number of bioinformatics tools (software). User convenience and availability are the determining factors of these tools being widely used in bioinformatics research. BLAST, FASTA (FAST-All), EMBOSS, ClustalW, RasMol and Protein Explorer, Cn3D, Swiss PDB viewer, Hex, Vega, Bioeditor etc. are commonly operated bioinformatics software tools in fisheries and aquaculture research. By default, these software tools mine and analyze a vast biological data set using the available databases. However, aquaculture scientists can use bioinformatics for genomic data manipulation, genome annotation and expression profiling, molecular folding, modeling, and design as well as generating biological network and system biology. Therefore, they can contribute in specified fields of aquaculture such as disease diagnosis and aquatic health management, fish nutritional aspects and culture-able strain development. Although having huge prospects, Bangladesh is still in infancy of applying bioinformatics in aquaculture research with limited resources. Research council at national level should be formed to bring all the enthusiastic scientists and skilled manpower under a single umbrella and facilitate to contribute in a collaborative platform. Besides, fully-fledged bioinformatics degree should be launched at University levels to produce knowledgeable and trained work force for future research. This review was attempted to shed light on bioinformatics, as young integrated field of bio-computational research, and its significance in aquaculture research of Bangladesh.
 Int. J. Agril. Res. Innov. Tech. 10(2): 137-145, December 2020

Type I interferonopathies with novel compound heterozygous TREX1 mutations in two siblings with different symptoms responded to tofacitinib

BackgroundType I interferonopathies are a group of rare autoimmune diseases characterised by excessive activation of type I interferon that leads to disturbances in immune function. Three prime repair exonuclease 1 (TREX1) is an important exonuclease and plays an important role in DNA damage repair. TREX1 mutations are associated with many type I interferonopathies. Studies have been published on the effectiveness of tofacitinib in the treatment of type I interferonopathies. The aim of this study is to identify the pathogenic variation in a Chinese family with type I interferonopathies and to observe the therapeutic effects of tofacitinib.MethodsA Chinese family with two members with type I interferonopathies was investigated. Whole exome sequencing and Sanger sequencing were applied for mutation screening using peripheral blood DNA of the patient and her family members. Sequencing results were analysed using bioinformatics software tools including VarCards and PolyPhen-2. Close clinical follow-up and observation were used to record changes in the disease before and after treatment with tofacitinib.ResultsCompound heterozygous variants of TREX1 were observed in the patient’s genome. One was a missense variant (NM_016381; c.C227T; p.Ala76Val) from the patient’s father, and the other was a frameshift variant (NM_016381; c.458dupA; p.Gln153Glnfs*3) from the patient’s mother. One of the proband’s elder brothers with similar skin lesions also carried these two variants. This brother of the proband had more serious cutaneous involvement with the comorbidity of cerebral palsy. These TREX1 variants have not been reported in previous studies and are predicted to be highly pathogenic. The proband was given tofacitinib that led to a marked improvement.ConclusionsWe identified two novel complex heterozygous variants in the TREX1 gene, which may underlie the molecular pathogenesis of the type I interferonopathies observed in members of this family. Tofacitinib could be an alternative treatment for this disease.

Abstract 3227: CLIFF, a bioinformatics software tool to explore molecular differences between two sets of cancer cell lines

Abstract Cancer cell lines are important models for drug tests and viability screens. For the evaluation and understanding of differences between two groups of cell lines, we created a research software application called CLIFF (cell line differences). CLIFF finds differences in mutational pattern or DNA copy number, lists and visualizes the significantly differentially expressed genes or proteins or performs Gene Set Enrichment Analysis (GSEA) to name a few. Today we have data from large panels of cancer cell lines consisting of gene-, protein- and microRNA expression, DNA copy number, DNA mutations, methylation, histone H3 modification, and concentration of metabolites, in addition to annotations of the tumor of origin. A number of cell line properties have been derived from these data, like microsatellite-(in)stability (MSS/MSI), mutation pattern classifications, or mutational burden. Many drugs have been tested and genes have been knocked in and out to study changes in molecular processes and proliferation. Even though cell lines in vitro do not represent the growth-properties and -conditions in a multi-cell-type tumor environment very well, they are indispensable cancer models in research and drug discovery. After testing or evaluating various properties of cell lines, like e.g. the viability dependency on short hairpin RNA (shRNA) knockdown or CRISPR-Cas9 knockout, as well as the expression of a specific gene, the presence or absence of a DNA mutation, or any other of the above-mentioned annotations, cell lines can be classified into two classes. In addition, users can upload cell line classifications based on external data, e.g. sensitivity or resistance to drug treatments or growth media formulations. Here are two examples of the application of CLIFF: (1) the association of knockdown or knockout of WRN in colorectal cell lines with microsatellite instability (MSI) could be confirmed. (2) The growth media has an important impact on the dependency on certain gene knockouts. It could be verified, that low concentrations of L-asparagine in the growth medium is associated with increased dependency on the asparagine producing enzyme asparagine synthetase (ASNS). In summary, we integrated various cell line data into CLIFF to provide sophisticated statistical analysis to researchers without deeper bioinformatics knowledge. This supports the understanding of molecular mechanisms of tumor growth, helps defining drug targets for treating cancer within defined patient populations, and accelerates our understanding of the molecular drivers of cancer. Citation Format: Andreas Wernitznig, Jesse J. Lipp, Thomas Zichner, Daniel Gerlach, Markus J. Bauer, Tilman Voss, Andreas Schlattl, Christian Haslinger, Philip G. Montgomery, Mahdi Zamanighomi, William R. Sellers, Norbert Kraut. CLIFF, a bioinformatics software tool to explore molecular differences between two sets of cancer cell lines [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3227.

From online resources to collaborative global neuroscience research: where are we heading?

Neuroscience has emerged as a richly transdisciplinary field, poised to leverage potential synergies with information technology. To investigate the complex nervous system in its normal function and the disease state, researchers in the field are increasingly reliant on generating, sharing and analyzing diverse data from multiple experimental paradigms at multiple spatial and temporal scales. There is growing recognition that brain function must be investigated from a systems perspective. This requires an integrated analysis of genomic, proteomic, anatomical, functional, topological and behavioural information to arrive at accurate scientific conclusions. The integrative neuroinformatics approaches for exploring complex structure-function relationships in the nervous system have been extensively reviewed. To support neuroscience research, the neuroscientific community also generates and maintains web-accessible databases of experimental and computational data and innovative software tools. Neuroinformatics is an emerging sub-field of neuroscience which focuses on addressing the unique technological and computational challenges to integrate and analyze the increasingly high-volume, multi-dimensional, and fine-grain data generated from neuroscience experiments. The most visible contributions from neuroinformatics include the myriad reference atlases of brain anatomy (human and other mammals such as rodents, primates and pig), gene and protein sequences and the bioinformatics software tools for alignment, matching and identification. Other neuroinformatics initiatives include the various open-source preprocessing and processing software and workflows for data analysis as well as the specifications for data format and software interoperability that allow seamless exchange of data between labs, software tools and modalities.

Metallo-ß-lactamases: a review.

Microbial pathogens including Enterobacteriaceae family members bear different antibiotic resistance genes comprising Extended-Spectrum-ß-Lactamases (ESBLs) and Metallo-ß-Lactamases (MBLs) on their chromosomes and mobile genetic elements such as plasmids and transposons. Because of the clinical concern regarding MBLs in global public healthcare system, this review focuses on different characteristics of MBLs. For preparing this review article, different databases, websites and search engines such as MEDLINE, SCOPUS, SCIENCEDIRECT and GOOGLE SCHOLAR were searched via MeSH keywords of Enterobacteriaceae, Escherichia coli, Klebsiella pneumoniae, MBL and Bioinformatics. Different types of papers comprising review articles and original articles which were published between the years of 1980 and 2020 were searched, studied and selected by the authors. The results show that, the importance of the spread of MBLs among microbial pathogens may lead to progressive studies for definite treatment. The use of computational biology and chemistry and bioinformatics has had effective consequences on recognition and identification of different properties of MBLs. The application of bioinformatic software tools and databases gives us a great promise regarding production of effective inhibitors against MBLs to have a definite treatment.

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assemblies exploiting protein sequence-based replicon distribution scores.

Plasmids are extrachromosomal genetic elements that replicate independently of the chromosome and play a vital role in the environmental adaptation of bacteria. Due to potential mobilization or conjugation capabilities, plasmids are important genetic vehicles for antimicrobial resistance genes and virulence factors with huge and increasing clinical implications. They are therefore subject to large genomic studies within the scientific community worldwide. As a result of rapidly improving next-generation sequencing methods, the quantity of sequenced bacterial genomes is constantly increasing, in turn raising the need for specialized tools to (i) extract plasmid sequences from draft assemblies, (ii) derive their origin and distribution, and (iii) further investigate their genetic repertoire. Recently, several bioinformatic methods and tools have emerged to tackle this issue; however, a combination of high sensitivity and specificity in plasmid sequence identification is rarely achieved in a taxon-independent manner. In addition, many software tools are not appropriate for large high-throughput analyses or cannot be included in existing software pipelines due to their technical design or software implementation. In this study, we investigated differences in the replicon distributions of protein-coding genes on a large scale as a new approach to distinguish plasmid-borne from chromosome-borne contigs. We defined and computed statistical discrimination thresholds for a new metric: the replicon distribution score (RDS), which achieved an accuracy of 96.6 %. The final performance was further improved by the combination of the RDS metric with heuristics exploiting several plasmid-specific higher-level contig characterizations. We implemented this workflow in a new high-throughput taxon-independent bioinformatics software tool called Platon for the recruitment and characterization of plasmid-borne contigs from short-read draft assemblies. Compared to PlasFlow, Platon achieved a higher accuracy (97.5 %) and more balanced predictions (F1=82.6 %) tested on a broad range of bacterial taxa and better or equal performance against the targeted tools PlasmidFinder and PlaScope on sequenced Escherichia coli isolates. Platon is available at: http://platon.computational.bio/.

Bioinformatics and Transcriptional Study of the Nramp Gene in the Extreme Acidophile Acidithiobacillus ferrooxidans Strain DC

The family of Nramp (natural resistance-associated macrophage protein) metal ion transporter functions in diverse organisms from bacteria to humans. Acidithiobacillus ferrooxidans (At. ferrooxidans) is a Gram-negative bacterium that lives at pH 2 in high concentrations of soluble ferrous ion (600 mM). The AFE_2126 protein of At. ferrooxidans of the Dachang Copper Mine (DC) was analyzed by bioinformatics software or online tools, showing that it was highly homologous to the Nramp family, and its subcellular localization was predicted to locate in the cytoplasmic membrane. Transcriptional study revealed that AFE_2126 was expressed by Fe2+-limiting conditions in At. ferrooxidans DC. It can be concluded that the AFE_2126 protein may function in ferrous ion transport into the cells. Based on the ΔpH of the cytoplasmic membrane between the periplasm (pH 3.5) and the cytoplasm (pH 6.5), it can be concluded that Fe2+ is transported in the direction identical to that of the H+ gradient. This study indirectly confirmed that the function of Nramp in At. ferrooxidans DC can transport divalent iron ions.

Bioinformatics and Molecular Analysis of the Breast Cancer Susceptibility Gene BRCA1 in Breast Cancer

Background: The breast cancer susceptibility gene (BRCA1) encodes a tumor suppressor protein which plays a vital role in the DNA damage repair and transcriptional regulation among other functions. Bioinformatics is a newly-emerged discipline that uses computer, mathematics, and statistics in molecular biology in order to analyze the large amounts of biological data quickly, freely, and accurately. Methods: The BRCA1 transcript (mRNA) levels were checked by using real-time quantitative polymerase chain reaction (RT-qPCR) in four sporadic breast cancer cell lines: MCF-7, T47D, MDA-MB-231, and MDA-MB-468 compared to the normal breast tissue. Bioinformatics tools were also used to compare and analyze different aspects of BRCA1 transcripts (multiple different mRNAs produced by a single gene) and splice variants (multiple proteins encoded by the same gene). Results: The level of BRCA1 mRNA was overexpressed in the studied breast cancer cell lines relative to the normal breast cDNA. Also, the bioinformatics software tools provided many important features of this gene that would help explain numerous controversial laboratory findings. Conclusions: The data presented here support a role for BRCA1 overexpression in the pathogenesis of sporadic breast cancer. The bioinformatics analysis of BRCA1 mRNA and protein variants can provide information essential for cancer diagnosis or therapy. The biological data gained from these tools can help authors make better decisions before launching expensive experiments.

Doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows

BackgroundSelecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.ResultsWe present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. Doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.ConclusionsOur proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.

An Integrated Bioinformatics and Quantitative Modeling Approach to Investigate Potential Claims of Oral Generic Drug Product Bioinequivalence: Introduction.

An Integrated Bioinformatics and Quantitative Modeling Approach to Investigate Potential Claims of Oral Generic Drug Product Bioinequivalence: Introduction.

Lipid Species Annotation at Double Bond Position Level with Custom Databases by Extension of the MZmine 2 Open-Source Software Package.

In recent years, proprietary and open-source bioinformatics software tools have been developed for the identification of lipids in complex biological samples based on high-resolution mass spectrometry data. These existent software tools often rely on publicly available lipid databases, such as LIPID MAPS, which, in some cases, only contain a limited number of lipid species for a specific lipid class. Other software solutions implement their own lipid species databases, which are often confined regarding implemented lipid classes, such as phospholipids. To address these drawbacks, we provide an extension of the widely used open-source metabolomics software MZmine 2, which enables the annotation of detected chromatographic features as lipid species. The extension is designed for straightforward generation of a custom database for selected lipid classes. Furthermore, each lipid's sum formula of the created database can be rapidly modified to search for derivatization products, oxidation products, in-source fragments, or adducts. The versatility will be exemplified by a liquid chromatography-high resolution mass spectrometry data set with postcolumn Paternò-Büchi derivatization. The derivatization reaction was performed to pinpoint the double bond positions in diacylglyceryltrimethylhomoserine lipid species in a lipid extract of a green algae ( Chlamydomonas reinhardtii) sample. The developed Lipid Search module extension of MZmine 2 supports the identification of lipids as far as double bond position level.

Bioinformatic methods for cancer neoantigen prediction.

Tumor cells accumulate aberrations not present in normal cells, leading to presentation of neoantigens on MHC molecules on their surface. These non-self neoantigens distinguish tumor cells from normal cells to the immune system and are thus targets for cancer immunotherapy. The rapid development of molecular profiling platforms, such as next-generation sequencing, has enabled the generation of large datasets characterizing tumor cells. The simultaneous development of algorithms has enabled rapid and accurate processing of these data. Bioinformatic software tools encoding the algorithms can be strung together in a workflow to identify neoantigens. Here, with a focus on high-throughput sequencing, we review state-of-the art bioinformatic tools along with the steps and challenges involved in neoantigen identification and recognition.

Comprehensive evaluation of RNA-seq analysis pipelines in diploid and polyploid species.

BackgroundThe usual analysis of RNA sequencing (RNA-seq) reads is based on an existing reference genome and annotated gene models. However, when a reference for the sequenced species is not available, alternatives include using a reference genome from a related species or reconstructing transcript sequences with de novo assembly. In addition, researchers are faced with many options for RNA-seq data processing and limited information on how their decisions will impact the final outcome. Using both a diploid and polyploid species with a distant reference genome, we have tested the influence of different tools at various steps of a typical RNA-seq analysis workflow on the recovery of useful processed data available for downstream analysis.FindingsAt the preprocessing step, we found error correction has a strong influence on de novo assembly but not on mapping results. After trimming, a greater percentage of reads could be used in downstream analysis by selecting gentle quality trimming performed with Skewer instead of strict quality trimming with Trimmomatic. This availability of reads correlated with size, quality, and completeness of de novo assemblies and with number of mapped reads. When selecting a reference genome from a related species to map reads, outcome was significantly improved when using mapping software tolerant of greater sequence divergence, such as Stampy or GSNAP.ConclusionsThe selection of bioinformatic software tools for RNA-seq data analysis can maximize quality parameters on de novo assemblies and availability of reads in downstream analysis.

MiRNA-BD: an evidence-based bioinformatics model and software tool for microRNA biomarker discovery

ABSTRACTMicroRNAs (miRNAs) are small non-coding RNAs with the potential as biomarkers for disease diagnosis, prognosis and therapy. In the era of big data and biomedical informatics, computer-aided biomarker discovery has become the current frontier. However, most of the computational models are highly dependent on specific prior knowledge and training-testing procedures, very few are mechanism-guided or evidence-based. To the best of our knowledge, untill now no general rules have been uncovered and applied to miRNA biomarker screening. In this study, we manually collected literature-reported cancer miRNA biomarkers and analyzed their regulatory patterns, including the regulatory modes, biological functions and evolutionary characteristics of their targets in the human miRNA-mRNA network. Two evidences were statistically detected and used to distinguish biomarker miRNAs from others. Based on these observations, we developed a novel bioinformatics model and software tool for miRNA biomarker discovery (http://sysbio.suda.edu.cn/MiRNA-BD/). In contrast to routine methods that focus on miRNA synergic functions, our method searches for vulnerable sites in the miRNA-mRNA network and considers the independent regulatory power of miRNAs, i.e., single-line regulations between miRNAs and mRNAs. The performance comparison demonstrates the generality and precision of our model, which identifies miRNA biomarkers for cancers as well as other complex diseases without training or specific prior knowledge.

Current Challenges in Plant Eco-Metabolomics.

The relatively new research discipline of Eco-Metabolomics is the application of metabolomics techniques to ecology with the aim to characterise biochemical interactions of organisms across different spatial and temporal scales. Metabolomics is an untargeted biochemical approach to measure many thousands of metabolites in different species, including plants and animals. Changes in metabolite concentrations can provide mechanistic evidence for biochemical processes that are relevant at ecological scales. These include physiological, phenotypic and morphological responses of plants and communities to environmental changes and also interactions with other organisms. Traditionally, research in biochemistry and ecology comes from two different directions and is performed at distinct spatiotemporal scales. Biochemical studies most often focus on intrinsic processes in individuals at physiological and cellular scales. Generally, they take a bottom-up approach scaling up cellular processes from spatiotemporally fine to coarser scales. Ecological studies usually focus on extrinsic processes acting upon organisms at population and community scales and typically study top-down and bottom-up processes in combination. Eco-Metabolomics is a transdisciplinary research discipline that links biochemistry and ecology and connects the distinct spatiotemporal scales. In this review, we focus on approaches to study chemical and biochemical interactions of plants at various ecological levels, mainly plant–organismal interactions, and discuss related examples from other domains. We present recent developments and highlight advancements in Eco-Metabolomics over the last decade from various angles. We further address the five key challenges: (1) complex experimental designs and large variation of metabolite profiles; (2) feature extraction; (3) metabolite identification; (4) statistical analyses; and (5) bioinformatics software tools and workflows. The presented solutions to these challenges will advance connecting the distinct spatiotemporal scales and bridging biochemistry and ecology.

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated ‘canned’ analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

Uso de herramientas para alineación de secuencias y creación de árboles filogenéticos para la determinación de especies

El artículo presente se enfoca en las herramientas de software y algoritmos de Bioinformática que se utilizan en la determinación de especies para mejorar y obtener resultados con mayor precisión. Este proyecto se realizó en colaboración con el centro de investigación de la Universidad Iberoamericana de Ciencia y Tecnología, de Santiago de Chile. Con el uso de programas de software que ejecutan algoritmos para crear árboles filogenéticos, se determinó que una muestra de una especie desconocida no necesariamente pertenece a una especie conocida, aunque esta sea idéntica morfológicamente.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes.

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

Explore the diagnosis value of whole exome sequencing in pediatric neuro-developmental disorders

Objective To evaluate the application value of whole exome sequencing (WES) in diagnosis of NDDs (neuro-developmental disorders) children. Metheod WES was used for the diagnosis of 35 unexplained NDD children, which admitted to the outpatient and ward of Children′s hospital affiliated to Capital institute of pediatric from November 2015 to November 2016. These children′s clinical data was collected detailedly. Using bioinformatics software tools combining with patient′s phenotype, the candidate genetic/genomic variants of these patients were identified from WES data. The final pathogenicity of genetic/genomic variants was interpreted according to the guideline of the American College of Medical Genetics and Genomics (ACMG), meanwhile, the variants validation and co-separation analysis in the parents and their family members were performed by Sanger sequencing, real time-PCR and multiplex ligation-dependent probe amplification (MLPA). Results 14 pathogenic single nucleotide variants (SNVs) and three pathogenic copy number variations (CNVs) were detected in the 35 NDD children, the detection rate in this study is 48.6%. Among the 14 pathogenic SNVs, 11 of them are the definite NDD-related genes according to OMIM database (such as CHARGE syndrome, Wiedemann-Steiner syndrome, Cockayne syndrome, etc.), and six of them are de novo (6/11, 54.6%). Three pathogenic CNVs were identified from WES data, including two microduplications and one microdeletion. Meanwhile, a female child carrying a frame shift mutation in MECP2 was found and the germline mosaicism with low-frequency mutation of this site (8.4%) was confirmed by his father's sperm. Conclusions The diagnosis rate of WES in NDDs children is 48.6% in our small-sample study. In addition to pathogenic/likely pathogenic SNVs, CNVs can be detected successfully from WES data, which effectively improved the diagnosis yield in NDDs children. (Chin J Lab Med, 2017, 40: 623-629) Key words: Developmental disabilites; Exome; Sequence analysis, DNA; Polymorphism, single nucleotide; DNA copy number variations