Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus, and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie.

SUMMARY: Although the ability to programmatically summarize and visually inspect sequencing data is an integral part of genome analysis, currently available methods are not capable of handling large numbers of samples. In particular, making a visual comparison of transcriptional landscapes between two sets of thousands of RNA-seq samples is limited by available computational resources, which can be overwhelmed due to the sheer size of the data. In this work, we present TieBrush, a software package designed to process very large sequencing datasets (RNA, whole-genome, exome, etc.) into a form that enables quick visual and computational inspection. TieBrush can also be used as a method for aggregating data for downstream computational analysis, and is compatible with most software tools that take aligned reads as input. AVAILABILITY AND IMPLEMENTATION: TieBrush is provided as a C++ package under the MIT License. Precompiled binaries, source code and example data are available on GitHub (https://github.com/alevar/tiebrush). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Comparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection. RESULTS: We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection. CONCLUSIONS: DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.

Since its first publication in 1998, Biological Sequence Analysis (BSA) by Durbin et al. has become a standard textbook for teaching bioinformatics. Part of the resources offered by BSA to understanding the underlying principles of bioinformatics is a set of workable exercises left to the interested reader to solve. Given the multidisciplinary background of bioinformatics, solving these problems requires integrating knowledge from various fields including genetics and molecular biology as well as mathematics and computer science. This is not an easy challenge for the numerous bioinformatics students who come with different abilities from a wide variety of educational backgrounds. Therefore I was glad to see this book offering not only step-by-step solutions to the problems presented in BSA but also to a large set of additional bioinformatics problems. From the first introductory chapter dealing with basic notions of probabilities to the more technical final chapter on more complex probabilistic concepts used in bioinformatics, this book follows exactly the structure of BSA. All problems presented in BSA are meticulously solved, and so are additional problems on topics concerning pairwise and multiple sequence alignments, hidden Markov models (HMMs) and building phylogenetic trees. The solutions often contain generalizations of particular cases and interesting remarks that draw the reader's attention to specific results important for the analysis of biological sequences. Most problems have analytical solutions, but some of them are algorithmic examples. In such cases, the less inexperienced computer programmer can benefit from the Web Supplemental Materials including Perl and C++ implementations of those algorithms. As in BSA each chapter is concluded with a ‘Further reading’ section with more up-to-date bibliographical references illustrating theoretical advances and research directions for the topics presented. I think that some of the most valuable parts of this book are the frequent theoretical introductions that broaden the fundamentals presented in BSA. The derivation of substitution matrices is briefly discussed in BSA. This is a critical step in scoring pairwise alignments, and I commend the authors for treating it thoroughly here. More attention is given to the PAM family in which case an ample discussion on the derivation of the amino acid substitution scores is followed by illustrative examples first in the context of a stationary Markov model, and later on in the context of an evolutionary model too. The statistical significance of pairwise alignments is addressed with exercises and theory topics describing the statistics of high-scoring pairs and the distribution of the length of the longest common word between random sequences. Theory and problems dealing with the selection of a particular probabilistic model and the estimation of its parameters expand the HMM chapter from BSA. More details are given for parameter estimation in the case of profile HMMs where a discrimination method for weighting training sequences is presented. Multiple sequence alignments are important not only to estimate the parameters of profile HMMs, but also in many other cases including phylogenetic inferences. I was glad to see the authors add three more exercises to the only one given for this topic in BSA. Probabilistic interpretations are abundant throughout the book and I found especially useful the treatment of realistic molecular evolution models that allow for insertions and deletions as well as variable rates at different sequence sites. This book is an excellent companion of the BSA book. Its mathematics is not simple but it would certainly help in developing the mathematical ability of the students studying BSA. By introducing detailed theoretical fundamentals this book extends BSA's domain and goes beyond a simple collection of bioinformatics problems with accompanying solutions. I recommend it to anyone interested in a more in-depth understanding of the bioinformatics of DNA and protein sequence analysis.

BACKGROUND: Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites. RESULTS: We have developed a new computational technique to identify significantly conserved motifs involved in splice site regulation. First, 84 putative exonic splicing enhancer hexamers are identified in Arabidopsis thaliana. Then a Gibbs sampling program called ELPH was used to locate conserved motifs represented by these hexamers in exonic regions near splice sites in confirmed genes. Oligomers containing 35 of these motifs have been shown experimentally to induce significant inclusion of A. thaliana exons. Second, integration of our regulatory motifs into two different splice site recognition programs significantly improved the ability of the software to correctly predict splice sites in a large database of confirmed genes. We have released GeneSplicerESE, the improved splice site recognition code, as open source software. CONCLUSION: Our results show that the use of the ESE motifs consistently improves splice site prediction accuracy.

MOTIVATION: The increased availability of genome sequences of closely related organisms has generated much interest in utilizing homology to improve the accuracy of gene prediction programs. Generalized pair hidden Markov models (GPHMMs) have been proposed as one means to address this need. However, all GPHMM implementations currently available are either closed-source or the details of their operation are not fully described in the literature, leaving a significant hurdle for others wishing to advance the state of the art in GPHMM design. RESULTS: We have developed an open-source GPHMM gene finder, TWAIN, which performs very well on two related Aspergillus species, A.fumigatus and A.nidulans, finding 89% of the exons and predicting 74% of the gene models exactly correctly in a test set of 147 conserved gene pairs. We describe the implementation of this GPHMM and we explicitly address the assumptions and limitations of the system. We suggest possible ways of relaxing those assumptions to improve the utility of the system without sacrificing efficiency beyond what is practical. AVAILABILITY: Available at http://www.tigr.org/software/pirate/twain/twain.html under the open-source Artistic License.

BACKGROUND: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. RESULTS: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. CONCLUSIONS: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.

UNLABELLED: We describe two new Generalized Hidden Markov Model implementations for ab initio eukaryotic gene prediction. The C/C++ source code for both is available as open source and is highly reusable due to their modular and extensible architectures. Unlike most of the currently available gene-finders, the programs are re-trainable by the end user. They are also re-configurable and include several types of probabilistic submodels which can be independently combined, such as Maximal Dependence Decomposition trees and interpolated Markov models. Both programs have been used at TIGR for the annotation of the Aspergillus fumigatus and Toxoplasma gondii genomes. AVAILABILITY: Source code and documentation are available under the open source Artistic License from http://www.tigr.org/software/pirate