RefSeq: an update on mammalian reference sequences

NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings

The Network of Cancer Genes (NCG, http://ncg.kcl.ac.uk/) is a manually curated repository of cancer genes derived from the scientific literature. Due to the increasing amount of cancer genomic data, we have introduced a more robust procedure to extract cancer genes from published cancer mutational screenings and two curators independently reviewed each publication. NCG release 5.0 (August 2015) collects 1571 cancer genes from 175 published studies that describe 188 mutational screenings of 13 315 cancer samples from 49 cancer types and 24 primary sites. In addition to collecting cancer genes, NCG also provides information on the experimental validation that supports the role of these genes in cancer and annotates their properties (duplicability, evolutionary origin, expression profile, function and interactions with proteins and miRNAs).

The potential clinical impact of the release of two drafts of the human proteome

The authors have carried out an investigation of the two "draft maps of the human proteome" published in 2014 in Nature. The findings include an abundance of poor spectra, low-scoring peptide-spectrum matches and incorrectly identified proteins in both these studies, highlighting clear issues with the application of false discovery rates. This noise means that the claims made by the two papers - the identification of high numbers of protein coding genes, the detection of novel coding regions and the draft tissue maps themselves - should be treated with considerable caution. The authors recommend that clinicians and researchers do not use the unfiltered data from these studies. Despite this these studies will inspire further investigation into tissue-based proteomics. As long as this future work has proper quality controls, it could help produce a consensus map of the human proteome and improve our understanding of the processes that underlie health and disease.

Pan-cancer transcriptome analysis reveals long noncoding RNAs with conserved function

A growing number of gene-centric studies have highlighted the emerging significance of lncRNAs in cancer. However, these studies primarily focus on a single cancer type. Therefore, we conducted a pan-cancer analysis of lncRNAs comparing tumor and matched normal expression levels using RNA-Seq data from ∼ 3,000 patients in 8 solid tumor types. While the majority of differentially expressed lncRNAs display tissue-specific expression we discovered 229 lncRNAs with outlier or differential expression across multiple cancers, which we refer to as 'onco-lncRNAs'. Due to their consistent altered expression, we hypothesize that these onco-lncRNAs may have conserved oncogenic and tumor suppressive functions across cancers. To address this, we associated the onco-lncRNAs in biological processes based on their co-expressed protein coding genes. To validate our predictions, we experimentally confirmed cell growth dependence of 2 novel oncogenic lncRNAs, onco-lncRNA-3 and onco-lncRNA-12, and a previously identified lncRNA CCAT1. Overall, we discovered lncRNAs that may have broad oncogenic and tumor suppressor roles that could significantly advance our understanding of cancer lncRNA biology.

Bovine Genome Database: new tools for gleaning function from the Bos taurus genome

We report an update of the Bovine Genome Database (BGD) (http://BovineGenome.org). The goal of BGD is to support bovine genomics research by providing genome annotation and data mining tools. We have developed new genome and annotation browsers using JBrowse and WebApollo for two Bos taurus genome assemblies, the reference genome assembly (UMD3.1.1) and the alternate genome assembly (Btau_4.6.1). Annotation tools have been customized to highlight priority genes for annotation, and to aid annotators in selecting gene evidence tracks from 91 tissue specific RNAseq datasets. We have also developed BovineMine, based on the InterMine data warehousing system, to integrate the bovine genome, annotation, QTL, SNP and expression data with external sources of orthology, gene ontology, gene interaction and pathway information. BovineMine provides powerful query building tools, as well as customized query templates, and allows users to analyze and download genome-wide datasets. With BovineMine, bovine researchers can use orthology to leverage the curated gene pathways of model organisms, such as human, mouse and rat. BovineMine will be especially useful for gene ontology and pathway analyses in conjunction with GWAS and QTL studies.

The Role of MicroRNAs as Predictors of Response to Tamoxifen Treatment in Breast Cancer Patients

Endocrine therapy is a key treatment strategy to control or eradicate hormone-responsive breast cancer. However, resistance to endocrine therapy leads to breast cancer relapse. The recent extension of adjuvant tamoxifen treatment up to 10 years actualizes the need for identifying biological markers that may be used to monitor predictors of treatment response. MicroRNAs are promising biomarkers that may fill the gap between preclinical knowledge and clinical observations regarding endocrine resistance. MicroRNAs regulate gene expression by posttranscriptional repression or degradation of mRNA, most often leading to gene silencing. MicroRNAs have been identified directly in the primary tumor, but also in the circulation of breast cancer patients. The few available studies investigating microRNA in patients suggest that seven microRNAs (miR-10a, miR-26, miR-30c, miR-126a, miR-210, miR-342 and miR-519a) play a role in tamoxifen resistance. Ingenuity Pathway Analysis (IPA) reveals that these seven microRNAs interact more readily with estrogen receptor (ER)-independent pathways than ER-related signaling pathways. Some of these pathways are targetable (e.g., PIK3CA), suggesting that microRNAs as biomarkers of endocrine resistance may have clinical value. Validation of the role of these candidate microRNAs in large prospective studies is warranted.

Current strategies for mutation detection in phenotype-driven screens utilising next generation sequencing

Mutagenesis-based screens in mice are a powerful discovery platform to identify novel genes or gene functions associated with disease phenotypes. An N-ethyl-N-nitrosourea (ENU) mutagenesis screen induces single nucleotide variants randomly in the mouse genome. Subsequent phenotyping of mutant and wildtype mice enables the identification of mutated pathways resulting in phenotypes associated with a particular ENU lesion. This unbiased approach to gene discovery conducts the phenotyping with no prior knowledge of the functional mutations. Before the advent of affordable next generation sequencing (NGS), ENU variant identification was a limiting step in gene characterization, akin to ‘finding a needle in a haystack’. The emergence of a reliable reference genome alongside advances in NGS has propelled ENU mutation discovery from an arduous, time-consuming exercise to an effective and rapid form of mutation discovery. This has permitted large mouse facilities worldwide to use ENU for novel mutation discovery in a high-throughput manner, helping to accelerate basic science at the mechanistic level. Here, we describe three different strategies used to identify ENU variants from NGS data and some of the subsequent steps for mutation characterisation.

Intron retention is a widespread mechanism of tumor-suppressor inactivation

A substantial fraction of disease-causing mutations are pathogenic through aberrant splicing. Although genome profiling studies have identified somatic single-nucleotide variants (SNVs) in cancer, the extent to which these variants trigger abnormal splicing has not been systematically examined. Here we analyzed RNA sequencing and exome data from 1,812 patients with cancer and identified ∼900 somatic exonic SNVs that disrupt splicing. At least 163 SNVs, including 31 synonymous ones, were shown to cause intron retention or exon skipping in an allele-specific manner, with ∼70% of the SNVs occurring on the last base of exons. Notably, SNVs causing intron retention were enriched in tumor suppressors, and 97% of these SNVs generated a premature termination codon, leading to loss of function through nonsense-mediated decay or truncated protein. We also characterized the genomic features predictive of such splicing defects. Overall, this work demonstrates that intron retention is a common mechanism of tumor-suppressor inactivation.

Generation of a de novo transcriptome from equine lamellar tissue

BACKGROUND

Laminitis, the structural failure of interdigitated tissue that suspends the distal skeleton within the hoof capsule, is a devastating disease that is the second leading cause of both lameness and euthanasia in the horse. Current transcriptomic research focuses on the expression of known genes. However, as this tissue is quite unique and equine gene annotation is largely derived from computational predictions, there are likely yet uncharacterized transcripts that may be involved in the etiology of laminitis. In order to create a novel annotation resource, we performed whole transcriptome sequencing of sagittal lamellar sections from one control and two laminitis affected horses.

RESULTS

Whole transcriptome sequencing of the three samples resulted in 113 million reads. Overall, 88 % of the reads mapped to the equCab2 reference genome, allowing for the identification of 119,430 SNPs. The de novo assembly generated around 75,000 transcripts, of which 36,000 corresponded to known annotations. Annotated transcript models are hosted in a public data repository and thus can be easily accessed or loaded into genome browsers. RT-PCR of 12 selected assemblies confirmed structure and expression in lamellar tissue.

CONCLUSIONS

Transcriptome sequencing represents a powerful tool to expand on equine annotation and identify novel targets for further laminitis research.

Alterations in the NF2/LATS1/LATS2/YAP Pathway in Schwannomas

Schwannomas are benign nerve sheath tumors composed of well-differentiated Schwann cells. Other than frequent NF2 (neurofibromatosis type 2) mutations (50%-60%), their molecular pathogenesis is not fully understood. LATS1 and LATS2 are downstream molecules of NF2 and are negative regulators of the yes-associated protein (YAP) oncogene in the Hippo signaling pathway. We assessed mutations of the NF2, LATS1, and LATS2 genes, promoter methylation of LATS1 and LATS2, and expression of YAP and phosphorylated YAP in 82 cases of sporadic schwannomas. Targeted sequencing using the Ion Torrent Proton instrument revealed NF2 mutations in 45 cases (55%), LATS1 mutations in 2 cases (2%), and LATS2 mutations in 1 case (1%) of schwannoma. Methylation-specific polymerase chain reaction showed promoter methylation of LATS1 and LATS2 in 14 cases (17%) and 25 cases (30%), respectively. Overall, 62 cases (76%) had at least 1 alteration in the NF2, LATS1, and/or LATS2 genes. Immunohistochemistry revealed nuclear YAP expression in 18 of 42 cases of schwannoma (43%) and reduced cytoplasmic phosphorylated YAP expression in 15 of 49 cases of schwannoma (31%), all of which had at least 1 alteration in the NF2, LATS1, and/or LATS2 genes. These results suggest that an abnormal Hippo signaling pathway is involved in the pathogenesis of most sporadic schwannomas.

Creating reference gene annotation for the mouse C57BL6/J genome assembly

Annotation on the reference genome of the C57BL6/J mouse has been an ongoing project ever since the draft genome was first published. Initially, the principle focus was on the identification of all protein-coding genes, although today the importance of describing long non-coding RNAs, small RNAs, and pseudogenes is recognized. Here, we describe the progress of the GENCODE mouse annotation project, which combines manual annotation from the HAVANA group with Ensembl computational annotation, alongside experimental and in silico validation pipelines from other members of the consortium. We discuss the more recent incorporation of next-generation sequencing datasets into this workflow, including the usage of mass-spectrometry data to potentially identify novel protein-coding genes. Finally, we will outline how the C57BL6/J genebuild can be used to gain insights into the variant sites that distinguish different mouse strains and species.

Functional classification of memory CD8(+) T cells by CX3CR1 expression

Localization of memory CD8(+) T cells to lymphoid or peripheral tissues is believed to correlate with proliferative capacity or effector function. Here we demonstrate that the fractalkine-receptor/CX3CR1 distinguishes memory CD8(+) T cells with cytotoxic effector function from those with proliferative capacity, independent of tissue-homing properties. CX3CR1-based transcriptome and proteome-profiling defines a core signature of memory CD8(+) T cells with effector function. We find CD62L(hi)CX3CR1(+) memory T cells that reside within lymph nodes. This population shows distinct migration patterns and positioning in proximity to pathogen entry sites. Virus-specific CX3CR1(+) memory CD8(+) T cells are scarce during chronic infection in humans and mice but increase when infection is controlled spontaneously or by therapeutic intervention. This CX3CR1-based functional classification will help to resolve the principles of protective CD8(+) T-cell memory.

The long noncoding RNA landscape in hypoxic and inflammatory renal epithelial injury

Long noncoding RNAs (lncRNAs) are emerging as key species-specific regulators of cellular and disease processes. To identify potential lncRNAs relevant to acute and chronic renal epithelial injury, we performed unbiased whole transcriptome profiling of human proximal tubular epithelial cells (PTECs) in hypoxic and inflammatory conditions. RNA sequencing revealed that the protein-coding and noncoding transcriptomic landscape differed between hypoxia-stimulated and cytokine-stimulated human PTECs. Hypoxia- and inflammation-modulated lncRNAs were prioritized for focused followup according to their degree of induction by these stress stimuli, their expression in human kidney tissue, and whether exposure of human PTECs to plasma of critically ill sepsis patients with acute kidney injury modulated their expression. For three lncRNAs (MIR210HG, linc-ATP13A4-8, and linc-KIAA1737-2) that fulfilled our criteria, we validated their expression patterns, examined their loci for conservation and synteny, and defined their associated epigenetic marks. The lncRNA landscape characterized here provides insights into novel transcriptomic variations in the renal epithelial cell response to hypoxic and inflammatory stress.

Enhanced virome sequencing using targeted sequence capture

Metagenomic shotgun sequencing (MSS) is an important tool for characterizing viral populations. It is culture independent, requires no a priori knowledge of the viruses in the sample, and may provide useful genomic information. However, MSS can lack sensitivity and may yield insufficient data for detailed analysis. We have created a targeted sequence capture panel, ViroCap, designed to enrich nucleic acid from DNA and RNA viruses from 34 families that infect vertebrate hosts. A computational approach condensed ∼1 billion bp of viral reference sequence into <200 million bp of unique, representative sequence suitable for targeted sequence capture. We compared the effectiveness of detecting viruses in standard MSS versus MSS following targeted sequence capture. First, we analyzed two sets of samples, one derived from samples submitted to a diagnostic virology laboratory and one derived from samples collected in a study of fever in children. We detected 14 and 18 viruses in the two sets, comprising 19 genera from 10 families, with dramatic enhancement of genome representation following capture enrichment. The median fold-increases in percentage viral reads post-capture were 674 and 296. Median breadth of coverage increased from 2.1% to 83.2% post-capture in the first set and from 2.0% to 75.6% in the second set. Next, we analyzed samples containing a set of diverse anellovirus sequences and demonstrated that ViroCap could be used to detect viral sequences with up to 58% variation from the references used to select capture probes. ViroCap substantially enhances MSS for a comprehensive set of viruses and has utility for research and clinical applications.

Tissue alkaline phosphatase is involved in lipid metabolism and gene expression and secretion of adipokines in adipocytes

BACKGROUND

Alkaline phosphatases are dimeric hydrolytic enzymes that dephosphorylate nucleotides and proteins. AP-TNAP is found primarily in skeletal tissues were it plays a major role in the mineralization of the extracellular matrix and bone formation.

METHODS

In this study we found through conventional and real time PCR assays that Alpl, the gene encoding for AP-TNAP is expressed in adipose tissue and in 3 T3-F442A adipocytes. We evaluated, using RNAi its role in adipocyte metabolism, and its cytoplasmic location by immunohistochemistry.

RESULTS

Alpl is highly expressed late in adipogenesis during adipose terminal differentiation. Knocking down Alpl increased the expression of the genes encoding for glycerophosphate dehydrogenase, and for the adipokines adiponectin, and FABP4 (aP2) but decreased that of leptin, and it also increased secretion of FABP4; these 3 proteins are important in adipocyte systemic signaling and insulin sensitivity. Inhibition of alkaline phosphatase activity in adipocytes by levamisole reduced lipolysis and the expression of various lipogenic genes. We found the enzyme intracytoplasmically, forming aggregates in close surroundings of the lipid droplets during lipolysis.

CONCLUSIONS

We suggest that AP-TNAP activity is involved in lipid and energy metabolism of fat cells, and it might regulate glucose metabolism and insulin sensitivity via adipokine synthesis and secretion.

GENERAL SIGNIFICANCE

The activity of AP-TNAP might have a critical role in the energy balance of the adipocyte, probably participating in obesity and metabolic syndrome.

Using mixtures of biological samples as process controls for RNA-sequencing experiments

Background

Genome-scale “-omics” measurements are challenging to benchmark due to the enormous variety of unique biological molecules involved. Mixtures of previously-characterized samples can be used to benchmark repeatability and reproducibility using component proportions as truth for the measurement. We describe and evaluate experiments characterizing the performance of RNA-sequencing (RNA-Seq) measurements, and discuss cases where mixtures can serve as effective process controls.

Results

We apply a linear model to total RNA mixture samples in RNA-seq experiments. This model provides a context for performance benchmarking. The parameters of the model fit to experimental results can be evaluated to assess bias and variability of the measurement of a mixture. A linear model describes the behavior of mixture expression measures and provides a context for performance benchmarking. Residuals from fitting the model to experimental data can be used as a metric for evaluating the effect that an individual step in an experimental process has on the linear response function and precision of the underlying measurement while identifying signals affected by interference from other sources. Effective benchmarking requires well-defined mixtures, which for RNA-Seq requires knowledge of the post-enrichment ‘target RNA’ content of the individual total RNA components. We demonstrate and evaluate an experimental method suitable for use in genome-scale process control and lay out a method utilizing spike-in controls to determine enriched RNA content of total RNA in samples.

Conclusions

Genome-scale process controls can be derived from mixtures. These controls relate prior knowledge of individual components to a complex mixture, allowing assessment of measurement performance. The target RNA fraction accounts for differential selection of RNA out of variable total RNA samples. Spike-in controls can be utilized to measure this relationship between target RNA content and input total RNA. Our mixture analysis method also enables estimation of the proportions of an unknown mixture, even when component-specific markers are not previously known, whenever pure components are measured alongside the mixture.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1912-7) contains supplementary material, which is available to authorized users.

The genome of the vervet (Chlorocebus aethiops sabaeus)

We describe a genome reference of the African green monkey or vervet (Chlorocebus aethiops). This member of the Old World monkey (OWM) superfamily is uniquely valuable for genetic investigations of simian immunodeficiency virus (SIV), for which it is the most abundant natural host species, and of a wide range of health-related phenotypes assessed in Caribbean vervets (C. a. sabaeus), whose numbers have expanded dramatically since Europeans introduced small numbers of their ancestors from West Africa during the colonial era. We use the reference to characterize the genomic relationship between vervets and other primates, the intra-generic phylogeny of vervet subspecies, and genome-wide structural variations of a pedigreed C. a. sabaeus population. Through comparative analyses with human and rhesus macaque, we characterize at high resolution the unique chromosomal fission events that differentiate the vervets and their close relatives from most other catarrhine primates, in whom karyotype is highly conserved. We also provide a summary of transposable elements and contrast these with the rhesus macaque and human. Analysis of sequenced genomes representing each of the main vervet subspecies supports previously hypothesized relationships between these populations, which range across most of sub-Saharan Africa, while uncovering high levels of genetic diversity within each. Sequence-based analyses of major histocompatibility complex (MHC) polymorphisms reveal extremely low diversity in Caribbean C. a. sabaeus vervets, compared to vervets from putatively ancestral West African regions. In the C. a. sabaeus research population, we discover the first structural variations that are, in some cases, predicted to have a deleterious effect; future studies will determine the phenotypic impact of these variations.

Aberrant sperm DNA methylation predicts male fertility status and embryo quality

OBJECTIVE

To evaluate whether male fertility status and/or embryo quality during in vitro fertilization (IVF) therapy can be predicted based on genomewide sperm deoxyribonucleic acid (DNA) methylation patterns.

DESIGN

Retrospective cohort study.

SETTING

University-based fertility center.

PATIENT(S)

Participants were 127 men undergoing IVF treatment (where any major female factor cause of infertility had been ruled out), and 54 normozoospermic, fertile men. The IVF patients were stratified into 2 groups: patients who had generally good embryogenesis and a positive pregnancy (n = 55), and patients with generally poor embryogenesis (n = 72; 42 positive and 30 negative pregnancies) after IVF.

INTERVENTION(S)

Genomewide sperm DNA methylation analysis was performed to measure methylation at >485,000 sites across the genome.

MAIN OUTCOME MEASURE(S)

A comparison was made of DNA methylation patterns of IVF patients vs. normozoospermic, fertile men.

RESULT(S)

Predictive models proved to be highly accurate in classifying male fertility status (fertile or infertile), with 82% sensitivity, and 99% positive predictive value. Hierarchic clustering identified clusters enriched for IVF patient samples and for poor-quality-embryo samples. Models built to identify samples within these groups, from neat samples, achieved positive predictive value ≥ 94% while identifying >one fifth of all IVF patient and poor-quality-embryo samples in each case. Using density gradient prepared samples, the same approach recovered 46% of poor-quality-embryo samples with no false positives.

CONCLUSION(S)

Sperm DNA methylation patterns differ significantly and consistently for infertile vs. fertile, normozoospermic men. In addition, DNA methylation patterns may be predictive of embryo quality during IVF.

DENdb: database of integrated human enhancers

Enhancers are cis-acting DNA regulatory regions that play a key role in distal control of transcriptional activities. Identification of enhancers, coupled with a comprehensive functional analysis of their properties, could improve our understanding of complex gene transcription mechanisms and gene regulation processes in general. We developed DENdb, a centralized on-line repository of predicted enhancers derived from multiple human cell-lines. DENdb integrates enhancers predicted by five different methods generating an enriched catalogue of putative enhancers for each of the analysed cell-lines. DENdb provides information about the overlap of enhancers with DNase I hypersensitive regions, ChIP-seq regions of a number of transcription factors and transcription factor binding motifs, means to explore enhancer interactions with DNA using several chromatin interaction assays and enhancer neighbouring genes. DENdb is designed as a relational database that facilitates fast and efficient searching, browsing and visualization of information. Database URL: http://www.cbrc.kaust.edu.sa/dendb/.

Utility of whole-genome sequencing for detection of newborn screening disorders in a population cohort of 1,696 neonates

PURPOSE

To assess the potential of whole-genome sequencing (WGS) to replicate and augment results from conventional blood-based newborn screening (NBS).

METHODS

Research-generated WGS data from an ancestrally diverse cohort of 1,696 infants and both parents of each infant were analyzed for variants in 163 genes involved in disorders included or under discussion for inclusion in US NBS programs. WGS results were compared with results from state NBS and related follow-up testing.

RESULTS

NBS genes are generally well covered by WGS. There is a median of one (range: 0-6) database-annotated pathogenic variant in the NBS genes per infant. Results of WGS and NBS in detecting 28 state-screened disorders and four hemoglobin traits were concordant for 88.6% of true positives (n = 35) and 98.9% of true negatives (n = 45,757). Of the five infants affected with a state-screened disorder, WGS identified two whereas NBS detected four. WGS yielded fewer false positives than NBS (0.037 vs. 0.17%) but more results of uncertain significance (0.90 vs. 0.013%).

CONCLUSION

WGS may help rule in and rule out NBS disorders, pinpoint molecular diagnoses, and detect conditions not amenable to current NBS assays.

Benchmark analysis of algorithms for determining and quantifying full-length mRNA splice forms from RNA-seq data

MOTIVATION

Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.

RESULTS

We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development.

AVAILABILITY AND IMPLEMENTATION

Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A computational strategy for predicting lineage specifiers in stem cell subpopulations

Stem cell differentiation is a complex biological event. Our understanding of this process is partly hampered by the co-existence of different cell subpopulations within a given population, which are characterized by different gene expression states driven by different underlying transcriptional regulatory networks (TRNs). Such cellular heterogeneity has been recently explored with the modern single-cell gene expression profiling technologies, such as single-cell RT-PCR and RNA-seq. However, the identification of cell subpopulation-specific TRNs and genes determining specific lineage commitment (i.e., lineage specifiers) remains a challenge due to the slower development of appropriate computational and experimental workflows. Here, we propose a computational method for predicting lineage specifiers for different cell subpopulations in binary-fate differentiation events. Our method first reconstructs subpopulation-specific TRNs, which is more realistic than reconstructing a single TRN representing multiple cell subpopulations. Then, it predicts lineage specifiers based on a model that assumes that each parental stem cell subpopulation is in a stable state maintained by its specific TRN stability core. In addition, this stable state is maintained in the parental cell subpopulation by the balanced gene expression pattern of pairs of opposing lineage specifiers for mutually exclusive different daughter cell subpopulations. To this end, we devised a statistical metric for identifying opposing lineage specifier pairs that show a significant ratio change upon differentiation. Application of this computational method to three different stem cell systems predicted known and putative novel lineage specifiers, which could be experimentally tested. Our method does not require pre-selection of putative candidate genes, and can be applied to any binary-fate differentiation system for which single-cell gene expression data are available. Furthermore, this method is compatible with both single-cell RT-PCR and single-cell RNA-seq data. Given the increasing importance of single-cell gene expression data in stem cell biology and regenerative medicine, approaches like ours would be useful for the identification of lineage specifiers and their associated TRN stability cores.

Leveraging transcript quantification for fast computation of alternative splicing profiles

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.

Arterivirus nsp12 versus the coronavirus nsp16 2'-O-methyltransferase: comparison of the C-terminal cleavage products of two nidovirus pp1ab polyproteins

The 3'-terminal domain of the most conserved ORF1b in three of the four families of the order Nidovirales (except for the family Arteriviridae) encodes a (putative) 2'-O-methyltransferase (2'-O-MTase), known as non structural protein (nsp) 16 in the family Coronaviridae and implicated in methylation of the 5' cap structure of nidoviral mRNAs. As with coronavirus transcripts, arterivirus mRNAs are assumed to possess a 5' cap although no candidate MTases have been identified thus far. To address this knowledge gap, we analysed the uncharacterized nsp12 of arteriviruses, which occupies the ORF1b position equivalent to that of the nidovirus 2'-O-MTase (coronavirus nsp16). In our in-depth bioinformatics analysis of nsp12, the protein was confirmed to be family specific whilst having diverged much further than other nidovirus ORF1b-encoded proteins, including those of the family Coronaviridae. Only one invariant and several partially conserved, predominantly aromatic residues were identified in nsp12, which may adopt a structure with alternating α-helices and β-strands, an organization also found in known MTases. However, no statistically significant similarity was found between nsp12 and the twofold larger coronavirus nsp16, nor could we detect MTase activity in biochemical assays using recombinant equine arteritis virus (EAV) nsp12. Our further analysis established that this subunit is essential for replication of this prototypic arterivirus. Using reverse genetics, we assessed the impact of 25 substitutions at 14 positions, yielding virus phenotypes ranging from WT-like to non-viable. Notably, replacement of the invariant phenylalanine 109 with tyrosine was lethal. We concluded that nsp12 plays an essential role during EAV replication, possibly by acting as a co-factor for another enzyme.

Novel Observations From Next-Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets

Understanding distinct gene expression patterns of normal adult and developing fetal human pancreatic α- and β-cells is crucial for developing stem cell therapies, islet regeneration strategies, and therapies designed to increase β-cell function in patients with diabetes (type 1 or 2). Toward that end, we have developed methods to highly purify α-, β-, and δ-cells from human fetal and adult pancreata by intracellular staining for the cell-specific hormone content, sorting the subpopulations by flow cytometry, and, using next-generation RNA sequencing, we report the detailed transcriptomes of fetal and adult α- and β-cells. We observed that human islet composition was not influenced by age, sex, or BMI, and transcripts for inflammatory gene products were noted in fetal β-cells. In addition, within highly purified adult glucagon-expressing α-cells, we observed surprisingly high insulin mRNA expression, but not insulin protein expression. This transcriptome analysis from highly purified islet α- and β-cell subsets from fetal and adult pancreata offers clear implications for strategies that seek to increase insulin expression in type 1 and type 2 diabetes.

Leveraging transcript quantification for fast computation of alternative splicing profiles

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.

Discovery of an essential nucleotidylating activity associated with a newly delineated conserved domain in the RNA polymerase-containing protein of all nidoviruses

RNA viruses encode an RNA-dependent RNA polymerase (RdRp) that catalyzes the synthesis of their RNA(s). In the case of positive-stranded RNA viruses belonging to the order Nidovirales, the RdRp resides in a replicase subunit that is unusually large. Bioinformatics analysis of this non-structural protein has now revealed a nidoviral signature domain (genetic marker) that is N-terminally adjacent to the RdRp and has no apparent homologs elsewhere. Based on its conservation profile, this domain is proposed to have nucleotidylation activity. We used recombinant non-structural protein 9 of the arterivirus equine arteritis virus (EAV) and different biochemical assays, including irreversible labeling with a GTP analog followed by a proteomics analysis, to demonstrate the manganese-dependent covalent binding of guanosine and uridine phosphates to a lysine/histidine residue. Most likely this was the invariant lysine of the newly identified domain, named nidovirus RdRp-associated nucleotidyltransferase (NiRAN), whose substitution with alanine severely diminished the described binding. Furthermore, this mutation crippled EAV and prevented the replication of severe acute respiratory syndrome coronavirus (SARS-CoV) in cell culture, indicating that NiRAN is essential for nidoviruses. Potential functions supported by NiRAN may include nucleic acid ligation, mRNA capping and protein-primed RNA synthesis, possibilities that remain to be explored in future studies.

Bioinformatics Annotation of Human Y Chromosome-Encoded Protein Pathways and Interactions

We performed a comprehensive analysis of human Y chromosome-encoded proteins, their pathways, and their interactions using bioinformatics tools. From the NCBI annotation release 107 of human genome, we retrieved a total of 66 proteins encoded on Y chromosome. Most of the retrieved proteins were also matched with the proteins listed in the core databases of the Human Proteome Project including neXtProt, PeptideAtlas, and the Human Protein Atlas. When we examined the pathways of human Y-encoded proteins through KEGG database and Pathway Studio software, many of proteins fall into the categories related to cell signaling pathways. Using the STRING program, we found a total of 49 human Y-encoded proteins showing strong/medium interaction with each other. While using the Pathway studio software, we found that a total of 16 proteins interact with other chromosome-encoded proteins. In particular, the SRY protein interacted with 17 proteins encoded on other chromosomes. Additionally, we aligned the sequences of human Y-encoded proteins with the sequences of chimpanzee and mouse Y-encoded proteins using the NCBI BLAST program. This analysis resulted in a significant number of orthologous proteins between human, chimpanzee, and mouse. Collectively, our findings provide the scientific community with additional information on the human Y chromosome-encoded proteins.

Prediction of Causal Candidate Genes in Coronary Artery Disease Loci

OBJECTIVE

Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes.

APPROACH AND RESULTS

All annotated genes in the loci were evaluated with respect to protein-coding single-nucleotide polymorphism and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously.

CONCLUSIONS

Our results substantially revise the list of likely candidates for CAD and suggest that genome-wide association studies efforts in other diseases may benefit from similar bioinformatics analyses.

Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates

Background

Retinitis pigmentosa (RP) is a highly heterogeneous genetic visual disorder with more than 70 known causative genes, some of them shared with other non-syndromic retinal dystrophies (e.g. Leber congenital amaurosis, LCA). The identification of RP genes has increased steadily during the last decade, and the 30% of the cases that still remain unassigned will soon decrease after the advent of exome/genome sequencing. A considerable amount of genetic and functional data on single RD genes and mutations has been gathered, but a comprehensive view of the RP genes and their interacting partners is still very fragmentary. This is the main gap that needs to be filled in order to understand how mutations relate to progressive blinding disorders and devise effective therapies.

Methodology

We have built an RP-specific network (RPGeNet) by merging data from different sources: high-throughput data from BioGRID and STRING databases, manually curated data for interactions retrieved from iHOP, as well as interactions filtered out by syntactical parsing from up-to-date abstracts and full-text papers related to the RP research field. The paths emerging when known RP genes were used as baits over the whole interactome have been analysed, and the minimal number of connections among the RP genes and their close neighbors were distilled in order to simplify the search space.

Conclusions

In contrast to the analysis of single isolated genes, finding the networks linking disease genes renders powerful etiopathological insights. We here provide an interactive interface, RPGeNet, for the molecular biologist to explore the network centered on the non-syndromic and syndromic RP and LCA causative genes. By integrating tissue-specific expression levels and phenotypic data on top of that network, a more comprehensive biological view will highlight key molecular players of retinal degeneration and unveil new RP disease candidates.

Comprehensive assembly of novel transcripts from unmapped human RNA-Seq data and their association with cancer

Crucial parts of the genome including genes encoding microRNAs and noncoding RNAs went unnoticed for years, and even now, despite extensive annotation and assembly of the human genome, RNA-sequencing continues to yield millions of unmappable and thus uncharacterized reads. Here, we examined > 300 billion reads from 536 normal donors and 1,873 patients encompassing 21 cancer types, identified ∼300 million such uncharacterized reads, and using a distinctive approach de novo assembled 2,550 novel human transcripts, which mainly represent long noncoding RNAs. Of these, 230 exhibited relatively specific expression or non-expression in certain cancer types, making them potential markers for those cancers, whereas 183 exhibited tissue specificity. Moreover, we used lentiviral-mediated expression of three selected transcripts that had higher expression in normal than in cancer patients and found that each inhibited the growth of HepG2 cells. Our analysis provides a comprehensive and unbiased resource of unmapped human transcripts and reveals their associations with specific cancers, providing potentially important new genes for therapeutic targeting.

Bipartite structure of the inactive mouse X chromosome

Background

In mammals, one of the female X chromosomes and all imprinted genes are expressed exclusively from a single allele in somatic cells. To evaluate structural changes associated with allelic silencing, we have applied a recently developed Hi-C assay that uses DNase I for chromatin fragmentation to mouse F1 hybrid systems.

Results

We find radically different conformations for the two female mouse X chromosomes. The inactive X has two superdomains of frequent intrachromosomal contacts separated by a boundary region. Comparison with the recently reported two-superdomain structure of the human inactive X shows that the genomic content of the superdomains differs between species, but part of the boundary region is conserved and located near the Dxz4/DXZ4 locus. In mouse, the boundary region also contains a minisatellite, Ds-TR, and both Dxz4 and Ds-TR appear to be anchored to the nucleolus. Genes that escape X inactivation do not cluster but are located near the periphery of the 3D structure, as are regions enriched in CTCF or RNA polymerase. Fewer short-range intrachromosomal contacts are detected for the inactive alleles of genes subject to X inactivation compared with the active alleles and with genes that escape X inactivation. This pattern is also evident for imprinted genes, in which more chromatin contacts are detected for the expressed allele.

Conclusions

By applying a novel Hi-C method to map allelic chromatin contacts, we discover a specific bipartite organization of the mouse inactive X chromosome that probably plays an important role in maintenance of gene silencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0728-8) contains supplementary material, which is available to authorized users.

Nuclear Fractionation Reveals Thousands of Chromatin-Tethered Noncoding RNAs Adjacent to Active Genes

A number of long noncoding RNAs (lncRNAs) have been reported to regulate transcription via recruitment of chromatin modifiers or bridging distal enhancer elements to gene promoters. However, the generality of these modes of regulation and the mechanisms of chromatin attachment for thousands of unstudied human lncRNAs remain unclear. To address these questions, we performed stringent nuclear fractionation coupled to RNA sequencing. We provide genome-wide identification of human chromatin-associated lncRNAs and demonstrate tethering of RNA to chromatin by RNAPII is a pervasive mechanism of attachment. We also uncovered thousands of chromatin-enriched RNAs (cheRNAs) that share molecular properties with known lncRNAs. Although distinct from eRNAs derived from active prototypical enhancers, the production of cheRNAs is strongly correlated with the expression of neighboring protein-coding genes. This work provides an updated framework for nuclear RNA organization that includes a large chromatin-associated transcript population correlated with active genes and may prove useful in de novo enhancer annotation.

Evolution of plant δ(1)-pyrroline-5-carboxylate reductases from phylogenetic and structural perspectives

Proline plays a crucial role in cell growth and stress responses, and its accumulation is essential for the tolerance of adverse environmental conditions in plants. Two routes are used to biosynthesize proline in plants. The main route uses glutamate as a precursor, while in the other route proline is derived from ornithine. The terminal step of both pathways, the conversion of δ(1)-pyrroline-5-carboxylate (P5C) to L-proline, is catalyzed by P5C reductase (P5CR) using NADH or NADPH as a cofactor. Since P5CRs are important housekeeping enzymes, they are conserved across all domains of life and appear to be relatively unaffected throughout evolution. However, global analysis of these enzymes unveiled significant functional diversity in the preference for cofactors (NADPH vs. NADH), variation in metal dependence and the differences in the oligomeric state. In our study we investigated evolutionary patterns through phylogenetic and structural analysis of P5CR representatives from all kingdoms of life, with emphasis on the plant species. We also attempted to correlate local sequence/structure variation among the functionally and structurally characterized members of the family.

Systematic Screening of Promoter Regions Pinpoints Functional Cis-Regulatory Mutations in a Cutaneous Melanoma Genome

With the recent discovery of recurrent mutations in the TERT promoter in melanoma, identification of other somatic causal promoter mutations is of considerable interest. Yet, the impact of sequence variation on the regulatory potential of gene promoters has not been systematically evaluated. This study assesses the impact of promoter mutations on promoter activity in the whole-genome sequenced malignant melanoma cell line COLO-829. Combining somatic mutation calls from COLO-829 with genome-wide chromatin accessibility and histone modification data revealed mutations within promoter elements. Interestingly, a high number of potential promoter mutations (n = 23) were found, a result mirrored in subsequent analysis of TCGA whole-melanoma genomes. The impact of wild-type and mutant promoter sequences were evaluated by subcloning into luciferase reporter vectors and testing their transcriptional activity in COLO-829 cells. Of the 23 promoter regions tested, four mutations significantly altered reporter activity relative to wild-type sequences. These data were then subjected to multiple computational algorithms that score the cis-regulatory altering potential of mutations. These analyses identified one mutation, located within the promoter region of NDUFB9, which encodes the mitochondrial NADH dehydrogenase (ubiquinone) 1 beta subcomplex 9, to be recurrent in 4.4% (19 of 432) of TCGA whole-melanoma exomes. The mutation is predicted to disrupt a highly conserved SP1/KLF transcription factor binding motif and its frequent co-occurrence with mutations in the coding sequence of NF1 supports a pathologic role for this mutation in melanoma. Taken together, these data show the relatively high prevalence of promoter mutations in the COLO-829 melanoma genome, and indicate that a proportion of these significantly alter the regulatory potential of gene promoters.

IMPLICATIONS

Genomic-based screening within gene promoter regions suggests that functional cis-regulatory mutations may be common in melanoma genomes, highlighting the need to examine their role in tumorigenesis.

Structural Bioinformatics Inspection of neXtProt PE5 Proteins in the Human Proteome

One goal of the Human Proteome Project is to identify at least one protein product for each of the ∼20,000 human protein-coding genes. As of October 2014, however, there are 3564 genes (18%) that have no or insufficient evidence of protein existence (PE), as curated by neXtProt; these comprise 2647 PE2-4 missing proteins and 616 PE5 dubious protein entries. We conducted a systematic examination of the 616 PE5 protein entries using cutting-edge protein structure and function modeling methods. Compared to a random sample of high-confidence PE1 proteins, the putative PE5 proteins were found to be over-represented in the membrane and cell surface proteins and peptides fold families. Detailed functional analyses show that most PE5 proteins, if expressed, would belong to transporters and receptors localized in the plasma membrane compartment. The results suggest that experimental difficulty in identifying membrane-bound proteins and peptides could have precluded their detection in mass spectrometry and that special enrichment techniques with improved sensitivity for membrane proteins could be important for the characterization of the PE5 "dark matter" of the human proteome. Finally, we identify 66 high scoring PE5 protein entries and find that six of them were reported in recent mass spectrometry databases; an illustrative annotation of these six is provided. This work illustrates a new approach to examine the potential folding and function of the dubious proteins comprising PE5, which we will next apply to the far larger group of missing proteins comprising PE2-4.

A Genome-Wide Landscape of Retrocopies in Primate Genomes

Gene duplication is a key factor contributing to phenotype diversity across and within species. Although the availability of complete genomes has led to the extensive study of genomic duplications, the dynamics and variability of gene duplications mediated by retrotransposition are not well understood. Here, we predict mRNA retrotransposition and use comparative genomics to investigate their origin and variability across primates. Analyzing seven anthropoid primate genomes, we found a similar number of mRNA retrotranspositions (∼7,500 retrocopies) in Catarrhini (Old Word Monkeys, including humans), but a surprising large number of retrocopies (∼10,000) in Platyrrhini (New World Monkeys), which may be a by-product of higher long interspersed nuclear element 1 activity in these genomes. By inferring retrocopy orthology, we dated most of the primate retrocopy origins, and estimated a decrease in the fixation rate in recent primate history, implying a smaller number of species-specific retrocopies. Moreover, using RNA-Seq data, we identified approximately 3,600 expressed retrocopies. As expected, most of these retrocopies are located near or within known genes, present tissue-specific and even species-specific expression patterns, and no expression correlation to their parental genes. Taken together, our results provide further evidence that mRNA retrotransposition is an active mechanism in primate evolution and suggest that retrocopies may not only introduce great genetic variability between lineages but also create a large reservoir of potentially functional new genomic loci in primate genomes.

Genome-wide analysis of enhancer RNA in gene regulation across 12 mouse tissues

Enhancers play a crucial role in gene regulation but the participation of enhancer transcripts (i.e. enhancer RNA, eRNAs) in regulatory systems remains unclear. We provide a computational analysis on eRNAs using genome-wide data across 12 mouse tissues. The expression of genes targeted by transcribing enhancer is positively correlated with eRNA expression and significantly higher than expression of genes targeted by non-transcribing enhancers. This result implies eRNA transcription indicates a state of enhancer that further increases gene expression. This state of enhancer is tissue-specific, as the same enhancer differentially transcribes eRNAs across tissues. Therefore, the presence of eRNAs describes a tissue-specific state of enhancer that is generally associated with higher expressed target genes, surmising as to whether eRNAs have gene activation potential. We further found a large number of eRNAs contain regions in which sequences and secondary structures are similar to microRNAs. Interestingly, an increasing number of recent studies hypothesize that microRNAs may switch from their general repressive role to an activating role when targeting promoter sequences. Collectively, our results provide speculation that eRNAs may be associated with the selective activation of enhancer target genes.

Mouse genome annotation by the RefSeq project

Complete and accurate annotation of the mouse genome is critical to the advancement of research conducted on this important model organism. The National Center for Biotechnology Information (NCBI) develops and maintains many useful resources to assist the mouse research community. In particular, the reference sequence (RefSeq) database provides high-quality annotation of multiple mouse genome assemblies using a combinatorial approach that leverages computation, manual curation, and collaboration. Implementation of this conservative and rigorous approach, which focuses on representation of only full-length and non-redundant data, produces high-quality annotation products. RefSeq records explicitly link sequences to current knowledge in a timely manner, updating public records regularly and rapidly in response to nomenclature updates, addition of new relevant publications, collaborator discussion, and user feedback. Whole genome re-annotation is also conducted at least every 12-18 months, and often more frequently in response to assembly updates or availability of informative data. This article highlights key features and advantages of RefSeq genome annotation products and presents an overview of NCBI processes to generate these data. Further discussion of NCBI's resources highlights useful features and the best methods for accessing our data.

Improved Methods to Generate Spheroid Cultures from Tumor Cells, Tumor Cells & Fibroblasts or Tumor-Fragments: Microenvironment, Microvesicles and MiRNA

Diagnostic and prognostic indicators are key components to achieve the goal of personalized cancer therapy. Two distinct approaches to this goal include predicting response by genetic analysis and direct testing of possible therapies using cultures derived from biopsy specimens. Optimally, the latter method requires a rapid assessment, but growing xenograft tumors or developing patient-derived cell lines can involve a great deal of time and expense. Furthermore, tumor cells have much different responses when grown in 2D versus 3D tissue environments. Using a modification of existing methods, we show that it is possible to make tumor-fragment (TF) spheroids in only 2–3 days. TF spheroids appear to closely model characteristics of the original tumor and may be used to assess critical therapy-modulating features of the microenvironment such as hypoxia. A similar method allows the reproducible development of spheroids from mixed tumor cells and fibroblasts (mixed-cell spheroids). Prior literature reports have shown highly variable development and properties of mixed-cell spheroids and this has hampered the detailed study of how individual tumor-cell components interact. In this study, we illustrate this approach and describe similarities and differences using two tumor models (U87 glioma and SQ20B squamous-cell carcinoma) with supporting data from additional cell lines. We show that U87 and SQ20B spheroids predict a key microenvironmental factor in tumors (hypoxia) and that SQ20B cells and spheroids generate similar numbers of microvesicles. We also present pilot data for miRNA expression under conditions of cells, tumors, and TF spheroids.

Rapid Detection of Rare Deleterious Variants by Next Generation Sequencing with Optional Microarray SNP Genotype Data

Autozygosity mapping is a powerful technique for the identification of rare, autosomal recessive, disease‐causing genes. The ease with which this category of disease gene can be identified has greatly increased through the availability of genome‐wide SNP genotyping microarrays and subsequently of exome sequencing. Although these methods have simplified the generation of experimental data, its analysis, particularly when disparate data types must be integrated, remains time consuming. Moreover, the huge volume of sequence variant data generated from next generation sequencing experiments opens up the possibility of using these data instead of microarray genotype data to identify disease loci. To allow these two types of data to be used in an integrated fashion, we have developed AgileVCFMapper, a program that performs both the mapping of disease loci by SNP genotyping and the analysis of potentially deleterious variants using exome sequence variant data, in a single step. This method does not require microarray SNP genotype data, although analysis with a combination of microarray and exome genotype data enables more precise delineation of disease loci, due to superior marker density and distribution.

Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer

Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets. Here, we describe an integrative proteogenomic method that extends the limit of proteogenomic searches to identify multiple variant peptides as well as immunoglobulin gene variations/rearrangements using customized mining of RNA-seq data. Our results also provide the first extensive characterization of tumor immune response and demonstrate the potential of this method to improve the molecular characterization of tumor subtypes.

Homo sapiens exhibit a distinct pattern of CNV genes regulation: an important role of miRNAs and SNPs in expression plasticity

Gene expression regulation is a complex and highly organized process involving a variety of genomic factors. It is widely accepted that differences in gene expression can contribute to the phenotypic variability between species, and that their interpretation can aid in the understanding of the physiologic variability. CNVs and miRNAs are two major players in the regulation of expression plasticity and may be responsible for the unique phenotypic characteristics observed in different lineages. We have previously demonstrated that a close interaction between these two genomic elements may have contributed to the regulation of gene expression during evolution. This work presents the molecular interactions between CNV and non CNV genes with miRNAs and other genomic elements in eight different species. A comprehensive analysis of these interactions indicates a unique nature of human CNV genes regulation as compared to other species. By using genes with short 3′ UTR that abolish the “canonical” miRNA-dependent regulation, as a model, we demonstrate a distinct and tight regulation of human genes that might explain some of the unique features of human physiology. In addition, comparison of gene expression regulation between species indicated that there is a significant difference between humans and mice possibly questioning the effectiveness of the latest as experimental models of human diseases.

Genome-Wide Copy Number Variations Using SNP Genotyping in a Mixed Breed Swine Population

Copy number variations (CNVs) are increasingly understood to affect phenotypic variation. This study uses SNP genotyping of trios of mixed breed swine to add to the catalog of known genotypic variation in an important agricultural animal. PorcineSNP60 BeadChip genotypes were collected from 1802 pigs that combined to form 1621 trios. These trios were from the crosses of 50 boars with 525 sows producing 1621 piglets. The pigs were part of a population that was a mix of ¼ Duroc, ½ Landrace and ¼ Yorkshire breeds. Merging the overlapping CNVs that were observed in two or more individuals to form CNV regions (CNVRs) yielded 502 CNVRs across the autosomes. The CNVRs intersected genes, as defined by RefSeq, 84% of the time – 420 out of 502. The results of this study are compared and contrasted to other swine studies using similar and different methods of detecting CNVR. While progress is being made in this field, more work needs to be done to improve consistency and confidence in CNVR results.

RefSeq curation and annotation of antizyme and antizyme inhibitor genes in vertebrates

Polyamines are ubiquitous cations that are involved in regulating fundamental cellular processes such as cell growth and proliferation; hence, their intracellular concentration is tightly regulated. Antizyme and antizyme inhibitor have a central role in maintaining cellular polyamine levels. Antizyme is unique in that it is expressed via a novel programmed ribosomal frameshifting mechanism. Conventional computational tools are unable to predict a programmed frameshift, resulting in misannotation of antizyme transcripts and proteins on transcript and genomic sequences. Correct annotation of a programmed frameshifting event requires manual evaluation. Our goal was to provide an accurately curated and annotated Reference Sequence (RefSeq) data set of antizyme transcript and protein records across a broad taxonomic scope that would serve as standards for accurate representation of these gene products. As antizyme and antizyme inhibitor proteins are functionally connected, we also curated antizyme inhibitor genes to more fully represent the elegant biology of polyamine regulation. Manual review of genes for three members of the antizyme family and two members of the antizyme inhibitor family in 91 vertebrate organisms resulted in a total of 461 curated RefSeq records.

A widespread role of the motif environment in transcription factor binding across diverse protein families

Transcriptional regulation requires the binding of transcription factors (TFs) to short sequence-specific DNA motifs, usually located at the gene regulatory regions. Interestingly, based on a vast amount of data accumulated from genomic assays, it has been shown that only a small fraction of all potential binding sites containing the consensus motif of a given TF actually bind the protein. Recent in vitro binding assays, which exclude the effects of the cellular environment, also demonstrate selective TF binding. An intriguing conjecture is that the surroundings of cognate binding sites have unique characteristics that distinguish them from other sequences containing a similar motif that are not bound by the TF. To test this hypothesis, we conducted a comprehensive analysis of the sequence and DNA shape features surrounding the core-binding sites of 239 and 56 TFs extracted from in vitro HT-SELEX binding assays and in vivo ChIP-seq data, respectively. Comparing the nucleotide content of the regions around the TF-bound sites to the counterpart unbound regions containing the same consensus motifs revealed significant differences that extend far beyond the core-binding site. Specifically, the environment of the bound motifs demonstrated unique sequence compositions, DNA shape features, and overall high similarity to the core-binding motif. Notably, the regions around the binding sites of TFs that belong to the same TF families exhibited similar features, with high agreement between the in vitro and in vivo data sets. We propose that these unique features assist in guiding TFs to their cognate binding sites.

Complex and multi-allelic copy number variation in human disease

Hundreds of copy number variants are complex and multi-allelic, in that they have many structural alleles and have rearranged multiple times in the ancestors who contributed chromosomes to current humans. Not only are the relationships of these multi-allelic CNVs (mCNVs) to phenotypes generally unknown, but many mCNVs have not yet been described at the basic levels—alleles, allele frequencies, structural features—that support genetic investigation. To date, most reported disease associations to these variants have been ascertained through candidate gene studies. However, only a few associations have reached the level of acceptance defined by durable replications in many cohorts. This likely stems from longstanding challenges in making precise molecular measurements of the alleles individuals have at these loci. However, approaches for mCNV analysis are improving quickly, and some of the unique characteristics of mCNVs may assist future association studies. Their various structural alleles are likely to have different magnitudes of effect, creating a natural allelic series of growing phenotypic impact and giving investigators a set of natural predictions and testable hypotheses about the extent to which each allele of an mCNV predisposes to a phenotype. Also, mCNVs’ low-to-modest correlation to individual single-nucleotide polymorphisms (SNPs) may make it easier to distinguish between mCNVs and nearby SNPs as the drivers of an association signal, and perhaps, make it possible to preliminarily screen candidate loci, or the entire genome, for the many mCNV–disease relationships that remain to be discovered.