GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene ...and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations.
Availability and implementation: GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license (
https://github.com/gpertea/gffread,
https://github.com/gpertea/gffcompare).
GTF (Gene Transfer Format) and GFF (General Feature Format) are popular file formats used by bioinformatics programs to represent and exchange information about various genomic features, such as gene ...and transcript locations and structure. GffRead and GffCompare are open source programs that provide extensive and efficient solutions to manipulate files in a GTF or GFF format. While GffRead can convert, sort, filter, transform, or cluster genomic features, GffCompare can be used to compare and merge different gene annotations.
Availability and implementation:
GFF utilities are implemented in C++ for Linux and OS X and released as open source under an MIT license (
https://github.com/gpertea/gffread
,
https://github.com/gpertea/gffcompare
).
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.
RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the ...sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new methods to handle the high error rate of long reads and offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of short-read assemblies. StringTie2 is more accurate and faster and uses less memory than all comparable short-read and long-read analysis tools.
Despite recent technological advances, the study of the human transcriptome is still in its early stages. Here we provide an overview of the complex human transcriptomic landscape, present the ...bioinformatics challenges posed by the vast quantities of transcriptomic data, and discuss some of the studies that have tried to determine how much of the human genome is transcribed. Recent evidence has suggested that more than 90% of the human genome is transcribed into RNA. However, this view has been strongly contested by groups of scientists who argued that many of the observed transcripts are simply the result of transcriptional noise. In this review, we conclude that the full extent of transcription remains an open question that will not be fully addressed until we decipher the complete range and biological diversity of the transcribed genomic sequences.
We assembled the sequences from deep RNA sequencing experiments by the Genotype-Tissue Expression (GTEx) project, to create a new catalog of human genes and transcripts, called CHESS. The new ...database contains 42,611 genes, of which 20,352 are potentially protein-coding and 22,259 are noncoding, and a total of 323,258 transcripts. These include 224 novel protein-coding genes and 116,156 novel transcripts. We detected over 30 million additional transcripts at more than 650,000 genomic loci, nearly all of which are likely nonfunctional, revealing a heretofore unappreciated amount of transcriptional noise in human cells. The CHESS database is available at http://ccb.jhu.edu/chess .
Many people expected the question 'How many genes in the human genome?' to be resolved with the publication of the genome sequence in 2001, but estimates continue to fluctuate.
RNA sequencing is widely used to measure gene expression across a vast range of animal and plant tissues and conditions. Most studies of computational methods for gene expression analysis use ...simulated data to evaluate the accuracy of these methods. These simulations typically include reads generated from known genes at varying levels of expression. Until now, simulations did not include reads from noisy transcripts, which might include erroneous transcription, erroneous splicing, and other processes that affect transcription in living cells. Here we examine the effects of realistic amounts of transcriptional noise on the ability of leading computational methods to assemble and quantify the genes and transcripts in an RNA sequencing experiment. We show that the inclusion of noise leads to systematic errors in the ability of these programs to measure expression, including systematic underestimates of transcript abundance levels and large increases in the number of false-positive genes and transcripts. Our results also suggest that alignment-free computational methods sometimes fail to detect transcripts expressed at relatively low levels.
Despite antiretroviral therapy (ART), human immunodeficiency virus (HIV)-1 persists in a stable latent reservoir, primarily in resting memory CD4(+) T cells. This reservoir presents a major barrier ...to the cure of HIV-1 infection. To purge the reservoir, pharmacological reactivation of latent HIV-1 has been proposed and tested both in vitro and in vivo. A key remaining question is whether virus-specific immune mechanisms, including cytotoxic T lymphocytes (CTLs), can clear infected cells in ART-treated patients after latency is reversed. Here we show that there is a striking all or none pattern for CTL escape mutations in HIV-1 Gag epitopes. Unless ART is started early, the vast majority (>98%) of latent viruses carry CTL escape mutations that render infected cells insensitive to CTLs directed at common epitopes. To solve this problem, we identified CTLs that could recognize epitopes from latent HIV-1 that were unmutated in every chronically infected patient tested. Upon stimulation, these CTLs eliminated target cells infected with autologous virus derived from the latent reservoir, both in vitro and in patient-derived humanized mice. The predominance of CTL-resistant viruses in the latent reservoir poses a major challenge to viral eradication. Our results demonstrate that chronically infected patients retain a broad-spectrum viral-specific CTL response and that appropriate boosting of this response may be required for the elimination of the latent reservoir.
Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome ...annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.
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