GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction.

High-throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request.

Minor spliceosome inactivation causes microcephaly, owing to cell cycle defects and death of self-amplifying radial glial cells.

Mutation in minor spliceosome components is linked to the developmental disorder microcephalic osteodysplastic primordial dwarfism type 1 (MOPD1). Here, we inactivated the minor spliceosome in the developing mouse cortex (pallium) by ablating Rnu11, which encodes the crucial minor spliceosome small nuclear RNA (snRNA) U11. Rnu11 conditional knockout mice were born with microcephaly, which was caused by the death of self-amplifying radial glial cells (RGCs), while intermediate progenitor cells and neurons were produced. RNA sequencing suggested that this cell death was mediated by upregulation of p53 (Trp53 - Mouse Genome Informatics) and DNA damage, which were both observed specifically in U11-null RGCs. Moreover, U11 loss caused elevated minor intron retention in genes regulating the cell cycle, which was consistent with fewer RGCs in S-phase and cytokinesis, alongside prolonged metaphase in RGCs. In all, we found that self-amplifying RGCs are the cell type most sensitive to loss of minor splicing. Together, these findings provide a potential explanation of how disruption of minor splicing might cause microcephaly in MOPD1.

Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data.

SUMMARY: This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2.bx.psu.edu/view/saharlcc/isoem2_isode2/. CONTACT: imandric1@student.gsu.edu or ion@engr.uconn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Network-based bioinformatics analysis of spatio-temporal RNA-Seq data reveals transcriptional programs underpinning normal and aberrant retinal development.

BACKGROUND: The retina as a model system with extensive information on genes involved in development/maintenance is of great value for investigations employing deep sequencing to capture transcriptome change over time. This in turn could enable us to find patterns in gene expression across time to reveal transition in biological processes. METHODS: We developed a bioinformatics pipeline to categorize genes based on their differential expression and their alternative splicing status across time by binning genes based on their transcriptional kinetics. Genes within same bins were then leveraged to query gene annotation databases to discover molecular programs employed by the developing retina. RESULTS: Using our pipeline on RNA-Seq data obtained from fractionated (nucleus/cytoplasm) developing retina at embryonic day (E) 16 and postnatal day (P) 0, we captured high-resolution as in the difference between the cytoplasm and the nucleus at the same developmental time. We found de novo transcription of genes whose transcripts were exclusively found in the nuclear transcriptome at P0. Further analysis showed that these genes enriched for functions that are known to be executed during postnatal development, thus showing that the P0 nuclear transcriptome is temporally ahead of that of its cytoplasm. We extended our strategy to perform temporal analysis comparing P0 data to either P21-Nrl-wildtype (WT) or P21-Nrl-knockout (KO) retinae, which predicted that the KO retina would have compromised vasculature. Indeed, histological manifestation of vasodilation has been reported at a later time point (P60). CONCLUSIONS: Thus, our approach was predictive of a phenotype before it presented histologically. Our strategy can be extended to investigating the development and/or disease progression of other tissue types.

Minor splicing snRNAs are enriched in the developing mouse CNS and are crucial for survival of differentiating retinal neurons.

In eukaryotes, gene expression requires splicing, which starts with the identification of exon-intron boundaries by the small, nuclear RNA (snRNAs) of the spliceosome, aided by associated proteins. In the mammalian genome, <1% of introns lack canonical exon-intron boundary sequences and cannot be spliced by the canonical splicing machinery. These introns are spliced by the minor spliceosome, consisting of unique snRNAs (U11, U12, U4atac, and U6atac). The importance of the minor spliceosome is underscored by the disease microcephalic osteodysplastic primordial dwarfism type 1 (MOPD1), which is caused by mutation in U4atac. Thus, it is important to understand the expression and function of the minor spliceosome and its targets in mammalian development, for which we used the mouse as our model. Here, we report enrichment of the minor snRNAs in the developing head/central nervous system (CNS) between E9.5 and E12.5, along with enrichment of these snRNAs in differentiating retinal neurons. Moreover, dynamic expression kinetics of minor intron-containing genes (MIGs) was observed across retinal development. DAVID analysis of MIGs that were cotranscriptionally upregulated embryonically revealed enrichment for RNA metabolism and cell cycle regulation. In contrast, MIGs that were cotranscriptionally upregulated postnatally revealed enrichment for protein localization/transport, vesicle-mediated transport, and calcium transport. Finally, we used U12 morpholino to inactivate the minor spliceosome in the postnatal retina, which resulted in apoptosis of differentiating retinal neurons. Taken together, our data suggest that the minor spliceosome may have distinct functions in embryonic versus postnatal development. Importantly, we show that the minor spliceosome is crucial for the survival of terminally differentiating retinal neurons.

Bootstrap-based differential gene expression analysis for RNA-Seq data with and without replicates.

A major application of RNA-Seq is to perform differential gene expression analysis. Many tools exist to analyze differentially expressed genes in the presence of biological replicates. Frequently, however, RNA-Seq experiments have no or very few biological replicates and development of methods for detecting differentially expressed genes in these scenarios is still an active research area. In this paper we introduce a novel method, called IsoDE, for differential gene expression analysis based on bootstrapping. We compared IsoDE against four existing methods (Fisher's exact test, GFOLD, edgeR and Cuffdiff) on RNA-Seq datasets generated using three different sequencing technologies, both with and without replicates. Experiments on MAQC RNA-Seq datasets without replicates show that IsoDE has consistently high accuracy as defined by the qPCR ground truth, frequently higher than that of the compared methods, particularly for low coverage data and at lower fold change thresholds. In experiments on RNA-Seq datasets with up to 7 replicates, IsoDE has also achieved high accuracy. Furthermore, unlike GFOLD and edgeR, IsoDE accuracy varies smoothly with the number of replicates, and is relatively uniform across the entire range of gene expression levels. The proposed non-parametric method based on bootstrapping has practical running time, and achieves robust performance over a broad range of technologies, number of replicates, sequencing depths, and minimum fold change thresholds.

Replication-dependent histone genes are actively transcribed in differentiating and aging retinal neurons.

In the mammalian genome, each histone family contains multiple replication-dependent paralogs, which are found in clusters where their transcription is thought to be coupled to the cell cycle. Here, we wanted to interrogate the transcriptional regulation of these paralogs during retinal development and aging. We employed deep sequencing, quantitative PCR, in situ hybridization (ISH), and microarray analysis, which revealed that replication-dependent histone genes were not only transcribed in progenitor cells but also in differentiating neurons. Specifically, by ISH analysis we found that different histone genes were actively transcribed in a subset of neurons between postnatal day 7 and 14. Interestingly, within a histone family, not all paralogs were transcribed at the same level during retinal development. For example, expression of Hist1h1b was higher embryonically, while that of Hist1h1c was higher postnatally. Finally, expression of replication-dependent histone genes was also observed in the aging retina. Moreover, transcription of replication-dependent histones was independent of rapamycin-mediated mTOR pathway inactivation. Overall, our data suggest the existence of variant nucleosomes produced by the differential expression of the replication-dependent histone genes across retinal development. Also, the expression of a subset of replication-dependent histone isotypes in senescent neurons warrants re-examining these genes as "replication-dependent." Thus, our findings underscore the importance of understanding the transcriptional regulation of replication-dependent histone genes in the maintenance and functioning of neurons.

Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity.

The mutational repertoire of cancers creates the neoepitopes that make cancers immunogenic. Here, we introduce two novel tools that identify, with relatively high accuracy, the small proportion of neoepitopes (among the hundreds of potential neoepitopes) that protect the host through an antitumor T cell response. The two tools consist of (a) the numerical difference in NetMHC scores between the mutated sequences and their unmutated counterparts, termed the differential agretopic index, and (b) the conformational stability of the MHC I-peptide interaction. Mechanistically, these tools identify neoepitopes that are mutated to create new anchor residues for MHC binding, and render the overall peptide more rigid. Surprisingly, the protective neoepitopes identified here elicit CD8-dependent immunity, even though their affinity for K(d) is orders of magnitude lower than the 500-nM threshold considered reasonable for such interactions. These results greatly expand the universe of target cancer antigens and identify new tools for human cancer immunotherapy.

Transcriptome assembly and quantification from Ion Torrent RNA-Seq data.

BACKGROUND: High throughput RNA sequencing (RNA-Seq) can generate whole transcriptome information at the single transcript level providing a powerful tool with multiple interrelated applications including transcriptome reconstruction and quantification. The sequences of novel transcripts can be reconstructed from deep RNA-Seq data, but this is computationally challenging due to sequencing errors, uneven coverage of expressed transcripts, and the need to distinguish between highly similar transcripts produced by alternative splicing. Another challenge in transcriptomic analysis comes from the ambiguities in mapping reads to transcripts. RESULTS: We present MaLTA, a method for simultaneous transcriptome assembly and quantification from Ion Torrent RNA-Seq data. Our approach explores transcriptome structure and incorporates a maximum likelihood model into the assembly and quantification procedure. A new version of the IsoEM algorithm suitable for Ion Torrent RNA-Seq reads is used to accurately estimate transcript expression levels. The MaLTA-IsoEM tool is publicly available at: http://alan.cs.gsu.edu/NGS/?q=malta CONCLUSIONS: Experimental results on both synthetic and real datasets show that Ion Torrent RNA-Seq data can be successfully used for transcriptome analyses. Experimental results suggest increased transcriptome assembly and quantification accuracy of MaLTA-IsoEM solution compared to existing state-of-the-art approaches.

Improved algorithms for parsing ESLTAGs: a grammatical model suitable for RNA pseudoknots.

Formal grammars have been employed in biology to solve various important problems. In particular, grammars have been used to model and predict RNA structures. Two such grammars are Simple Linear Tree Adjoining Grammars (SLTAGs) and Extended SLTAGs (ESLTAGs). Performances of techniques that employ grammatical formalisms critically depend on the efficiency of the underlying parsing algorithms. In this paper, we present efficient algorithms for parsing SLTAGs and ESLTAGs. Our algorithm for SLTAGs parsing takes O(min{m,n4}) time and O(min{m,n4}) space, where m is the number of entries that will ever be made in the matrix M (that is normally used by TAG parsing algorithms). Our algorithm for ESLTAGs parsing takes O(min{m,n4}) time and O(min{m,n4}) space. We show that these algorithms perform better, in practice, than the algorithms of Uemura et al.