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.

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.

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: , 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.

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.

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: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: [email protected] Supplimentary Information: Supplementary data are available at Bioinformatics online.

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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A small set of genes showed reduced expression in mites on 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.

Frequent Subgraph Mining is important in many scientific fields including molecular biology, genomics, and chemistry. Graphs are used to represent specific structures, and by finding common subgraphs one can infer common properties. Identification of maximum frequent subgraphs, therefore, provides an approach to reveal hidden knowledge shared among graphs, and classify graphs into informative categories. Most existing algorithms explicitly explore the frequent subgraph space by depth-first or breadth-first search, and traverse the entire lattice of subgraphs. However, explicit search for subgraph isomorphism is NP-complete, and limited to small graphs. We propose MMC-Margin, a Metropolis Monte Carlo approach that extends the idea of the Margin search space to greatly reduce time and space complexity. We apply our method to three domain examples: RNA structure graphs from actual biological structures, artificially constructed RNA graphs with known common structures, and structures of chemical mutagens. Our experiments show that MMC-Margin effectively identifies maximum frequent patterns in subgraph spaces where exact algorithms encounter time and space issues. Specifically, we find that MMC-Margin can find the maximum common subgraph in these datasets in as little as 120 seconds, whereas, a complete search with Margin takes hundreds or even thousands of CPU hours. In addition to time efficiency, the MCMC approach requires little memory. The MMC-margin approach described here allows the identification of common subgraphs in biological and chemical datasets and the expansion of using subgraph isomorphism in extracting latent information.

We provide a 2007 update on the bioinformatics research in the Asia-Pacific from the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998. From 2002, APBioNet has organized the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2007 Conference was organized as the 6th annual conference of the Asia-Pacific Bioinformatics Network, on Aug. 27-30, 2007 at Hong Kong, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea) and New Delhi (India). Besides a scientific meeting at Hong Kong, satellite events organized are a pre-conference training workshop at Hanoi, Vietnam and a post-conference workshop at Nansha, China. This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. We have organized the papers into thematic areas, highlighting the growing contribution of research excellence from this region, to global bioinformatics endeavours.

In 1998, the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation was set up to champion the advancement of bioinformatics in the Asia Pacific. By 2002, APBioNet was able to gain sufficient critical mass to initiate the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2006 Conference was organized as the 5 th annual conference of the Asia-Pacific Bioinformatics Network, on Dec. 18–20, 2006 in New Delhi, India, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand) and Busan (South Korea). This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. It exemplifies a typical snapshot of the growing research excellence in bioinformatics of the region as we embark on a trajectory of establishing a solid bioinformatics research culture in the Asia Pacific that is able to contribute fully to the global bioinformatics community.

The Wiggle series are support vector machine-based predictors that identify regions of functional flexibility using only protein sequence information. Functionally flexible regions are defined as regions that can adopt different conformational states and are assumed to be necessary for bioactivity. Many advances have been made in understanding the relationship between protein sequence and structure. This work contributes to those efforts by making strides to understand the relationship between protein sequence and flexibility. A coarse-grained protein dynamic modeling approach was used to generate the dataset required for support vector machine training. We define our regions of interest based on the participation of residues in correlated large-scale fluctuations. Even with this structure-based approach to computationally define regions of functional flexibility, predictors successfully extract sequence-flexibility relationships that have been experimentally confirmed to be functionally important. Thus, a sequence-based tool to identify flexible regions important for protein function has been created. The ability to identify functional flexibility using a sequence based approach complements structure-based definitions and will be especially useful for the large majority of proteins with unknown structures. The methodology offers promise to identify structural genomics targets amenable to crystallization and the possibility to engineer more flexible or rigid regions within proteins to modify their bioactivity.

I n 1993, the Intelligent Systems for Molecular Biology conference was successfully launched as an annual meeting for scientific exchange within the nascent interdisciplinary science of computational biology.Demand grew stronger each year for an umbrella organization that extended its reach beyond a once-a-year conference.The International Society for Computational Biology (ISCB) was formed in 1997 to provide computational biologists and bioinformaticians worldwide with a community of peers with whom they could interact year-round in their mutual quest to advance the understanding of living systems through computation.Over the past 13 years, the ISCB has grown and evolved along with the fields of computational biology and bioinformatics.As the society's president, I am proud to report that our membership now comprises nearly 2,000 members in over 50 countries.Each year, PLoS Computational Biology, as the official journal of the ISCB, will publish the bylaws of the society.These are the legally registered rules by which the elected and appointed leaders of the society, along with the membership at large, must abide.It is my hope that ISCB members will take the time to read these rules, and to understand, thereby, the legal framework in which ISCB operates.Clearly, as ISCB grows, these rules will be adapted to changing circumstances-knowing the current rules is a first step to making any improvements to them.We also believe that our bylaws may be useful to other groups trying to form similar organizations.

SUMMARY: 2HAPI (version 2 of High density Array Pattern Interpreter) is a web-based, publicly-available analytical tool designed to aid researchers in microarray data analysis. 2HAPI includes tools for searching, manipulating, visualizing, and clustering the large sets of data generated by microarray experiments. Other features include association of genes with NCBI information and linkage to external data resources. Unique to 2HAPI is the ability to retrieve upstream sequences of co-regulated genes for promoter analysis using MEME (Multiple Expectation-maximization for Motif Elicitation) AVAILABILITY: 2HAPI is freely available at http://array.sdsc.edu. Users can try 2HAPI anonymously with pre-loaded data or they can register as a 2HAPI user and upload their data.

Abstract Motivation: High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. Results: We describe a data mining method that uses indexing terms (‘keywords’) from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes. Availability: We have created a publicly accessible website that provides this form of gene expression interpretation at http://www.array.ucsd.edu. Contact: [email protected] 8 To whom correspondence should be addressed at: University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92093-0602, USA. 7 Present address: Genomics Institute of the Novartis Research Foundation, 3115 Merryfield Row, San Diego, CA 92121, USA.

The huge success of the 8th International Conference on Intelligent Systems for Molecular Biology, ISMB-2000 (http://ismb2000.sdsc.edu),indicates that bioinformatics and computational biology have truly come of age.The ISMB-2000 meeting attracted over 1200 attendees from 44 countries and registration had to be closed due to lack of space.We believe the incredible demand for this conference reflects the growing acceptance of bioinformatics as a scientific discipline, as well as the still growing need for trained professionals to deal with the growth in biological data.The completion of the human genome marks a unique point in time for biology.The reductionist paradigm will soon have been pushed to its ultimate limit, in which we will have identified every gene, every message, and every protein in the cell.The challenge now is to build synthetic and predictive models that will enable us to understand the complexity of living organisms as holistic systems.ISMB-2000 placed special emphasis on knowledge discovery from the modeling and simulation of complex biological systems.This included, but was not limited to, interpretation of large-scale gene expression data, whole genome comparative analysis, and mathematical modeling of biochemical pathways.This seemed appropriate given the completion of the human genome and thus the continual reference to the post-genomic era.Bioinformatics remains a notably international discipline.Over 140 papers were received, with approximately 60% coming from the US, 30% from Europe and 10% from other parts of the world.Forty-two papers were selected for oral presentation.In addition keynote addresses were given by

MOTIVATION: To illustrate an intuitive and statistically valid method for combining independent sources of evidence that yields a p-value for the complete evidence, and to apply it to the problem of detecting simultaneous matches to multiple patterns in sequence homology searches. RESULTS: In sequence analysis, two or more (approximately) independent measures of the membership of a sequence (or sequence region) in some class are often available. We would like to estimate the likelihood of the sequence being a member of the class in view of all the available evidence. An example is estimating the significance of the observed match of a macromolecular sequence (DNA or protein) to a set of patterns (motifs) that characterize a biological sequence family. An intuitive way to do this is to express each piece of evidence as a p-value, and then use the product of these p-values as the measure of membership in the family. We derive a formula and algorithm (QFAST) for calculating the statistical distribution of the product of n independent p-values. We demonstrate that sorting sequences by this p-value effectively combines the information present in multiple motifs, leading to highly accurate and sensitive sequence homology searches.

Profile analysis measures the similarity between a target sequence and a group of aligned sequences (the probe). The probe sequences are used to produce a position-specific scoring table (the profile) that can be aligned with any sequence (the target) using standard dynamic programming methods. We are developing a library of profiles, each describing a different structural motif. This allows any target sequence to be rapidly scanned for the presence of structural motifs. Levels of significance for the comparison of target sequences with the profile are determined in advance, permitting an objective decision to be made as to whether a protein is likely to possess a structural motif.