Bibliometric review of ATAC-Seq and its application in gene expression.

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Chromatin-enriched RNAs mark active and repressive cis-regulation: An analysis of nuclear RNA-seq.

Long noncoding RNAs (lncRNAs) localize in the cell nucleus and influence gene expression through a variety of molecular mechanisms. Chromatin-enriched RNAs (cheRNAs) are a unique class of lncRNAs that are tightly bound to chromatin and putatively function to locally cis-activate gene transcription. CheRNAs can be identified by biochemical fractionation of nuclear RNA followed by RNA sequencing, but until now, a rigorous analytic pipeline for nuclear RNA-seq has been lacking. In this study, we survey four computational strategies for nuclear RNA-seq data analysis and develop a new pipeline, Tuxedo-ch, which outperforms other approaches. Tuxedo-ch assembles a more complete transcriptome and identifies cheRNA with higher accuracy than other approaches. We used Tuxedo-ch to analyze benchmark datasets of K562 cells and further characterize the genomic features of intergenic cheRNA (icheRNA) and their similarity to enhancer RNAs (eRNAs). We quantify the transcriptional correlation of icheRNA and adjacent genes and show that icheRNA is more positively associated with neighboring gene expression than eRNA or cap analysis of gene expression (CAGE) signals. We also explore two novel genomic associations of cheRNA, which indicate that cheRNAs may function to promote or repress gene expression in a context-dependent manner. IcheRNA loci with significant levels of H3K9me3 modifications are associated with active enhancers, consistent with the hypothesis that enhancers are derived from ancient mobile elements. In contrast, antisense cheRNA (as-cheRNA) may play a role in local gene repression, possibly through local RNA:DNA:DNA triple-helix formation.

miRNA-mediated macrophage behaviors responding to matrix stiffness and ox-LDL.

Atherosclerosis is one of the leading causes of morbidity and mortality, mainly due to the immune response triggered by the recruitment of monocytes/macrophages in the artery wall. Accumulating evidence have shown that matrix stiffness and oxidized low-density lipoproteins (ox-LDL) play important roles in atherosclerosis through modulating cellular behaviors. However, whether there is a synergistic effect for ox-LDL and matrix stiffness on macrophages behavior has not been explored yet. In this study, we developed a model system to investigate the synergistic role of ox-LDL and matrix stiffness on macrophage behaviors, such as migration, inflammatory and apoptosis. We found that there was a matrix stiffness-dependent behavior of monocyte-derived macrophages stimulated with ox-LDL. What's more, macrophages were more sensitive to ox-LDL on the stiff matrices compared to cells cultured on the soft matrices. Through next-generation sequencing, we identified miRNAs in response to matrix stiffness and ox-LDL and predicted pathways that showed the capability of miRNAs in directing macrophages fates. Our study provides a novel understanding of the important synergistic role of ox-LDL and matrix stiffness in modulating macrophages behaviors, especially through miRNAs signaling pathways, which could be potential key regulators in atherosclerosis and immune-targeted therapies.

MiPepid: MicroPeptide identification tool using machine learning.

BACKGROUND: Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirmed. In the past decade, a growing number of micropeptides have been shown to play significant roles in vital biological activities. Despite the increased amount of data, we still lack bioinformatics tools for specifically identifying micropeptides from DNA sequences. Indeed, most existing tools for classifying coding and noncoding ORFs were built on datasets in which "normal-sized" proteins were considered to be positives and short ORFs were generally considered to be noncoding. Since the functional and biophysical constraints on small peptides are likely to be different from those on "normal" proteins, methods for predicting short translated ORFs must be trained independently from those for longer proteins. RESULTS: In this study, we have developed MiPepid, a machine-learning tool specifically for the identification of micropeptides. We trained MiPepid using carefully cleaned data from existing databases and used logistic regression with 4-mer features. With only the sequence information of an ORF, MiPepid is able to predict whether it encodes a micropeptide with 96% accuracy on a blind dataset of high-confidence micropeptides, and to correctly classify newly discovered micropeptides not included in either the training or the blind test data. Compared with state-of-the-art coding potential prediction methods, MiPepid performs exceptionally well, as other methods incorrectly classify most bona fide micropeptides as noncoding. MiPepid is alignment-free and runs sufficiently fast for genome-scale analyses. It is easy to use and is available at https://github.com/MindAI/MiPepid. CONCLUSIONS: MiPepid was developed to specifically predict micropeptides, a category of proteins with increasing significance, from DNA sequences. It shows evident advantages over existing coding potential prediction methods on micropeptide identification. It is ready to use and runs fast.

IRESpy: an XGBoost model for prediction of internal ribosome entry sites.

BACKGROUND: Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5' cap-dependent translation initiation mechanism. IRES usually function when 5' cap-dependent translation initiation has been blocked or repressed. They have been widely found to play important roles in viral infections and cellular processes. However, a limited number of confirmed IRES have been reported due to the requirement for highly labor intensive, slow, and low efficiency laboratory experiments. Bioinformatics tools have been developed, but there is no reliable online tool. RESULTS: This paper systematically examines the features that can distinguish IRES from non-IRES sequences. Sequence features such as kmer words, structural features such as QMFE, and sequence/structure hybrid features are evaluated as possible discriminators. They are incorporated into an IRES classifier based on XGBoost. The XGBoost model performs better than previous classifiers, with higher accuracy and much shorter computational time. The number of features in the model has been greatly reduced, compared to previous predictors, by including global kmer and structural features. The contributions of model features are well explained by LIME and SHapley Additive exPlanations. The trained XGBoost model has been implemented as a bioinformatics tool for IRES prediction, IRESpy (https://irespy.shinyapps.io/IRESpy/), which has been applied to scan the human 5' UTR and find novel IRES segments. CONCLUSIONS: IRESpy is a fast, reliable, high-throughput IRES online prediction tool. It provides a publicly available tool for all IRES researchers, and can be used in other genomics applications such as gene annotation and analysis of differential gene expression.

Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis.

Motivation: With the decreased cost of RNA-Seq, an increasing number of non-model organisms have been sequenced. Due to the lack of reference genomes, de novo transcriptome assembly is required. However, there is limited systematic research evaluating the quality of de novo transcriptome assemblies and how the assembly quality influences downstream analysis. Results: We used two authentic RNA-Seq datasets from Arabidopsis thaliana, and produced transcriptome assemblies using eight programs with a series of k-mer sizes (from 25 to 71), including BinPacker, Bridger, IDBA-tran, Oases-Velvet, SOAPdenovo-Trans, SSP, Trans-ABySS and Trinity. We measured the assembly quality in terms of reference genome base and gene coverage, transcriptome assembly base coverage, number of chimeras and number of recovered full-length transcripts. SOAPdenovo-Trans performed best in base coverage, while Trans-ABySS performed best in gene coverage and number of recovered full-length transcripts. In terms of chimeric sequences, BinPacker and Oases-Velvet were the worst, while IDBA-tran, SOAPdenovo-Trans, Trans-ABySS and Trinity produced fewer chimeras across all single k-mer assemblies. In differential gene expression analysis, about 70% of the significantly differentially expressed genes (DEG) were the same using reference genome and de novo assemblies. We further identify four reasons for the differences in significant DEG between reference genome and de novo transcriptome assemblies: incomplete annotation, exon level differences, transcript fragmentation and incorrect gene annotation, which we suggest that de novo assembly is beneficial even when a reference genome is available. Availability and Implementation: Software used in this study are publicly available at the authors' websites. Contact: gribskov@purdue.edu Supplimentary Information: Supplementary data are available at Bioinformatics online.

AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs.

MOTIVATION: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. RESULTS: We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. AVAILABILITY AND IMPLEMENTATION: The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . CONTACT: gribskov@purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DEIsoM: a hierarchical Bayesian model for identifying differentially expressed isoforms using biological replicates.

MOTIVATION: High-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy. RESULTS: We present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/hao-peng/DEIsoM. CONTACT: pengh@alumni.purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CellMiner Companion: an interactive web application to explore CellMiner NCI-60 data.

UNLABELLED: The NCI-60 human tumor cell line panel is an invaluable resource for cancer researchers, providing drug sensitivity, molecular and phenotypic data for a range of cancer types. CellMiner is a web resource that provides tools for the acquisition and analysis of quality-controlled NCI-60 data. CellMiner supports queries of up to 150 drugs or genes, but the output is an Excel file for each drug or gene. This output format makes it difficult for researchers to explore the data from large queries. CellMiner Companion is a web application that facilitates the exploration and visualization of output from CellMiner, further increasing the accessibility of NCI-60 data. AVAILABILITY AND IMPLEMENTATION: The web application is freely accessible at https://pul-bioinformatics.shinyapps.io/CellMinerCompanion The R source code can be downloaded at https://github.com/pepascuzzi/CellMinerCompanion.git CONTACT: ppascuzz@purdue.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GenoBase: comprehensive resource database of Escherichia coli K-12.

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources.

Differential gene expression in Varroa jacobsoni mites following a host shift to European honey bees (Apis mellifera).

BACKGROUND: Varroa mites are widely considered the biggest honey bee health problem worldwide. Until recently, Varroa jacobsoni has been found to live and reproduce only in Asian honey bee (Apis cerana) colonies, while V. destructor successfully reproduces in both A. cerana and A. mellifera colonies. However, we have identified an island population of V. jacobsoni that is highly destructive to A. mellifera, the primary species used for pollination and honey production. The ability of these populations of mites to cross the host species boundary potentially represents an enormous threat to apiculture, and is presumably due to genetic variation that exists among populations of V. jacobsoni that influences gene expression and reproductive status. In this work, we investigate differences in gene expression between populations of V. jacobsoni reproducing on A. cerana and those either reproducing or not capable of reproducing on A. mellifera, in order to gain insight into differences that allow V. jacobsoni to overcome its normal species tropism. RESULTS: We sequenced and assembled a de novo transcriptome of V. jacobsoni. We also performed a differential gene expression analysis contrasting biological replicates of V. jacobsoni populations that differ in their ability to reproduce on A. mellifera. Using the edgeR, EBSeq and DESeq R packages for differential gene expression analysis, we found 287 differentially expressed genes (FDR ≤ 0.05), of which 91% were up regulated in mites reproducing on A. mellifera. In addition, mites found reproducing on A. mellifera showed substantially more variation in expression among replicates. We searched for orthologous genes in public databases and were able to associate 100 of these 287 differentially expressed genes with a functional description. CONCLUSIONS: There is differential gene expression between the two mite groups, with more variation in gene expression among mites that were able to reproduce on A. mellifera. A small set of genes showed reduced expression in mites on the A. mellifera host, including putative transcription factors and digestive tract developmental genes. The vast majority of differentially expressed genes were up-regulated in this host. This gene set showed enrichment for genes associated with mitochondrial respiratory function and apoptosis, suggesting that mites on this host may be experiencing higher stress, and may be less optimally adapted to parasitize it. Some genes involved in reproduction and oogenesis were also overexpressed, which should be further studied in regards to this host shift.

Ontogenetic changes in embryonic and brain gene expression in progeny produced from migratory and resident Oncorhynchus mykiss.

Little information has been gathered regarding the ontogenetic changes that contribute to differentiation between resident and migrant individuals, particularly before the onset of gross morphological and physiological changes in migratory individuals. The aim of this study was to evaluate gene expression during early development in Oncorhynchus mykiss populations with different life histories, in a tissue known to integrate environmental cues to regulate complex developmental processes and behaviours. We sampled offspring produced from migrant and resident parents, collecting whole embryos prior to the beginning of first feeding, and brain tissue at three additional time points over the first year of development. RNA sequencing for 32 individuals generated a reference transcriptome of 30 177 genes that passed count thresholds. Differential gene expression between migrant and resident offspring was observed for 1982 genes. The greatest number of differentially expressed genes occurred at 8 months of age, in the spring a full year before the obvious physiological transformation from stream-dwelling parr to sea water-adaptable smolts begins for migrant individuals. Sex and age exhibited considerable effects on differential gene expression between migrants and resident offspring. Differential gene expression was observed in genes previously associated with migration, but also in genes previously unassociated with early life history divergence. Pathway analysis revealed coordinated differential expression in genes related to phototransduction, which could modulate photoperiod responsiveness and variation in circadian rhythms. The role for early differentiation in light sensitivity and biological rhythms is particularly intriguing in understanding early brain processes involved in differentiation of migratory and resident life history types.

A physical interaction network of dengue virus and human proteins.

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection.

miRNome expression analysis in canine diffuse large B-cell lymphoma.

Introduction: Lymphoma is a common canine cancer with translational relevance to human disease. Diffuse large B-cell lymphoma (DLBCL) is the most frequent subtype, contributing to almost fifty percent of clinically recognized lymphoma cases. Identifying new biomarkers capable of early diagnosis and monitoring DLBCL is crucial for enhancing remission rates. This research seeks to advance our knowledge of the molecular biology of DLBCL by analyzing the expression of microRNAs, which regulate gene expression by negatively impacting gene expression via targeted RNA degradation or translational repression. The stability and accessibility of microRNAs make them appropriate biomarkers for the diagnosis, prognosis, and monitoring of diseases. Methods: We extracted and sequenced microRNAs from ten fresh-frozen lymph node tissue samples (six DLBCL and four non-neoplastic). Results: Small RNA sequencing data analysis revealed 35 differently expressed miRNAs (DEMs) compared to controls. RT-qPCR confirmed that 23/35 DEMs in DLBCL were significantly upregulated (n = 14) or downregulated (n = 9). Statistical significance was determined by comparing each miRNA's average expression fold-change (2-Cq) between the DLCBL and healthy groups by applying the unpaired parametric Welch's 2-sample t-test and false discovery rate (FDR). The predicted target genes of the DEMs were mainly enriched in the PI3K-Akt-MAPK pathway. Discussion: Our data point to the potential value of miRNA signatures as diagnostic biomarkers and serve as a guideline for subsequent experimental studies to determine the targets and functions of these altered miRNAs in canine DLBCL.

Identifying complete RNA structural ensembles including pseudoknots.

The close relationship between RNA structure and function underlines the significance of accurately predicting RNA structures from sequence information. Structural topologies such as pseudoknots are of particular interest due to their ubiquity and direct involvement in RNA function, but identifying pseudoknots is a computationally challenging problem and existing heuristic approaches usually perform poorly for RNA sequences of even a few hundred bases. We survey the performance of pseudoknot prediction methods on a data set of full-length RNA sequences representing varied sequence lengths, and biological RNA classes such as RNase P RNA, Group I Intron, tmRNA and tRNA. Pseudoknot prediction methods are compared with minimum free energy and suboptimal secondary structure prediction methods in terms of correct base-pairs, stems and pseudoknots and we find that the ensemble of suboptimal structure predictions succeeds in identifying correct structural elements in RNA that are usually missed in MFE and pseudoknot predictions. We propose a strategy to identify a comprehensive set of non-redundant stems in the suboptimal structure space of a RNA molecule by applying heuristics that reduce the structural redundancy of the predicted suboptimal structures by merging slightly varying stems that are predicted to form in local sequence regions. This reduced-redundancy set of structural elements consistently outperforms more specialized approaches.in data sets. Thus, the suboptimal folding space can be used to represent the structural diversity of an RNA molecule more comprehensively than optimal structure prediction approaches alone.

The genome of Nectria haematococca: contribution of supernumerary chromosomes to gene expansion.

The ascomycetous fungus Nectria haematococca, (asexual name Fusarium solani), is a member of a group of >50 species known as the "Fusarium solani species complex". Members of this complex have diverse biological properties including the ability to cause disease on >100 genera of plants and opportunistic infections in humans. The current research analyzed the most extensively studied member of this complex, N. haematococca mating population VI (MPVI). Several genes controlling the ability of individual isolates of this species to colonize specific habitats are located on supernumerary chromosomes. Optical mapping revealed that the sequenced isolate has 17 chromosomes ranging from 530 kb to 6.52 Mb and that the physical size of the genome, 54.43 Mb, and the number of predicted genes, 15,707, are among the largest reported for ascomycetes. Two classes of genes have contributed to gene expansion: specific genes that are not found in other fungi including its closest sequenced relative, Fusarium graminearum; and genes that commonly occur as single copies in other fungi but are present as multiple copies in N. haematococca MPVI. Some of these additional genes appear to have resulted from gene duplication events, while others may have been acquired through horizontal gene transfer. The supernumerary nature of three chromosomes, 14, 15, and 17, was confirmed by their absence in pulsed field gel electrophoresis experiments of some isolates and by demonstrating that these isolates lacked chromosome-specific sequences found on the ends of these chromosomes. These supernumerary chromosomes contain more repeat sequences, are enriched in unique and duplicated genes, and have a lower G+C content in comparison to the other chromosomes. Although the origin(s) of the extra genes and the supernumerary chromosomes is not known, the gene expansion and its large genome size are consistent with this species' diverse range of habitats. Furthermore, the presence of unique genes on supernumerary chromosomes might account for individual isolates having different environmental niches.

Spontaneous symmetry breaking in genome evolution.

The quest for evolutionary mechanisms providing separation between the coding (exons) and noncoding (introns) parts of genomic DNA remains an important focus of genetics. This work combines an analysis of the most recent achievements of genomics and fundamental concepts of random processes to provide a novel point of view on genome evolution. Exon sizes in sequenced genomes show a lognormal distribution typical of a random Kolmogoroff fractioning process. This implies that the process of intron incretion may be independent of exon size, and therefore could be dependent on intron-exon boundaries. All genomes examined have two distinctive classes of exons, each with different evolutionary histories. In the framework proposed in this article, these two classes of exons can be derived from a hypothetical ancestral genome by (spontaneous) symmetry breaking. We note that one of these exon classes comprises mostly alternatively spliced exons.

Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq.

RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years, even though, there are several programs or websites that assist researchers to analyze and visualize NGS results, they have several limitations. Therefore, Shiny-DEG, a web application that facilitates the exploration and visualization of differentially expressed genes from RNA-seq, was developed. It integrates multi-factor design experiments, allows users to modify the parameters interactively according to experiments purpose and all analysis results can be downloaded directly, aiming to further assisting the interpretation and explanation of the biological questions. Therefore, it serves better for biologists without programming skills. Overall, this project is of great significance to reveal the mechanism of transcriptome differences.

The Evolution History of Fe-S Cluster A-Type Assembly Protein Reveals Multiple Gene Duplication Events and Essential Protein Motifs.

Iron-sulfur (Fe-S) clusters play important roles in electron transfer, metabolic and biosynthetic reactions, and the regulation of gene expression. Understanding the biogenesis of Fe-S clusters is therefore relevant to many fields. In the complex process of Fe-S protein formation, the A-type assembly protein (ATAP) family, which consists of several subfamilies, plays an essential role in Fe-S cluster formation and transfer and is highly conserved across the tree of life. However, the taxonomic distribution, motif compositions, and the evolutionary history of the ATAP subfamilies are not well understood. To address these problems, our study investigated the taxonomic distribution of 321 species from a broad cross-section of taxa. Then, we identified common and specific motifs in multiple ATAP subfamilies to explain the functional conservation and nonredundancy of the ATAPs, and a novel, essential motif was found in Eumetazoa IscA1, which has a newly found magnetic function. Finally, we used phylogenetic analytical methods to reconstruct the evolution history of this family. Our results show that two types of ErpA proteins (nonproteobacteria-type ErpA1 and proteobacteria-type ErpA2) exist in bacteria. The ATAP family, consisting of seven subfamilies, can be further classified into two types of ATAPs. Type-I ATAPs include IscA, SufA, HesB, ErpA1, and IscA1, with an ErpA1-like gene as their last common ancestor, whereas type-II ATAPs consist of ErpA2 and IscA2, duplicated from an ErpA2-like gene. During the mitochondrial endosymbiosis, IscA became IscA1 in eukaryotes and ErpA2 became IscA2 in eukaryotes, respectively.

Transcriptome analysis identifies metallothionein as biomarkers to predict recurrence in hepatocellular cacinoma.

BACKGROUND: Liver cancer is the fifth most common cancer, and hepatocellular carcinoma (HCC) is the major liver tumor type seen in adults. HCC is usually caused by chronic liver disease such as hepatitis B virus or hepatitis C virus infection. One of the promising treatments for HCC is liver transplantation, in which a diseased liver is replaced with a healthy liver from another person. However, recurrence of HCC after surgery is a significant problem. Therefore, it is important to discover reliable cellular biomarkers that can predict recurrence in HCC. METHODS: We analyzed previously published HCC RNA-Seq data that includes 21 paired tumor and normal samples, in which nine tumors were recurrent after orthotopic liver transplantation and 12 were nonrecurrent tumors with their paired normal samples. We used both the reference genome and de novo transcriptome assembly based analyses to identify differentially expressed genes (DEG) and used RandomForest to discover biomarkers. RESULTS: We obtained 398 DEG using the Reference approach and 412 DEG using de novo assembly approach. Among these DEG, 258 genes were identified by both approaches. We further identified 30 biomarkers that could predict the recurrence. We used another independent HCC study that includes 50 patients normal and tumor samples. By using these 30 biomarkers, the prediction accuracy was 100% for normal condition and 98% for tumor condition. A group of Metallothionein was specifically discovered as biomarkers in both reference and de novo assembly approaches. CONCLUSION: We identified a group of Metallothionein genes as biomarkers to predict recurrence. The metallothionein genes were all down-regulated in tumor samples, suggesting that low metallothionein expression may be a promoter of tumor growth. In addition, using de novo assembly identified some unique biomarkers, further confirmed the necessity of conducting a de novo assembly in human cancer study.