Set Of Human Genes Research Articles

Structure, clustering and functional insights of repeats configurations in the upstream promoter region of the human coding genes

BackgroundRepetitive DNA sequences (Repeats) are significant regions in the human genome that have a specific genomic distribution, structure, and several binding sites for genome architecture and function. In consequence, the possible configurations of Repeats in specific and dynamic regions like the gene promoters could define footprints for molecular mechanisms, pathways, and cell function beyond their density in the genome. Here we explored the distribution of Repeats in the upstream promoter region of the human coding genes with the aim to identify specific configurations, clusters and functional meaning of those elements. Our method includes structural descriptions, hierarchical clustering, pathway association, and functional enrichment analysis.ResultsWe report here several configurations of Repeats in the upstream promoter region (UPR), which define 2729 patterns for the 80% of the human coding genes. There are 47 types of Repeats in these configurations, where the most frequent were Alu, Low_complexity, MIR, Simple_repeat, LINE/L2, LINE/L1, hAT-Charlie, and ERV1. The distribution, length, and the high frequency of Repeats in the UPR defines several patterns and clusters, where the minimum frequency of configuration among Repeats was higher than 0.7. We found those clusters associated with cellular pathways and ontologies; thus, it was plausible to determine groups of Repeats to specific functional insights, for example, pathways for Genetic Information Processing or Metabolism shows particular groups of Repeats with specific configurations.ConclusionBased on these findings, we propose that specific configurations of repetitive elements describe frequent patterns in the upstream promoter for sets of human coding genes, which those correlated to specific and essential cell pathways and functions.

Low level DUX4 expression disrupts myogenesis through deregulation of myogenic gene expression

Loss of silencing of the DUX4 gene on chromosome 4 causes facioscapulohumeral muscular dystrophy. While high level DUX4 expression induces apoptosis, the effects of low level DUX4 expression on human myogenic cells are not well understood. Low levels and sporadic expression of DUX4 have been reported in FSHD biopsy samples and myoblast cultures. Here, we show that a large set of human myogenic genes is rapidly deregulated by DUX4, including MYOD1 and MYF5, which are efficiently repressed even by low, non-toxic levels of DUX4. Human myoblasts modified to express low nontoxic levels of DUX4 were significantly impaired from differentiating into myotubes in vitro. Surprisingly, inhibition of differentiation does not require the transcriptional activation domain, thus is likely a feature of all mammalian DUX genes. DUX4 does not bind near the MYF5 gene, but has a prominent ChIP-seq peak within the MYF5 −118 kb enhancer. We find that when DUX4 binds at this site, it directs enhancer activity towards a nearby transcriptional start site for a noncoding nonfunctional RNA we name DIME (DUX4-induced MYF5 enhancer) transcript. These data highlight the anti-myogenic properties of DUX4 in human myogenic progenitor cells, and provide an example of enhancer disruption in the downregulation of MYF5.

The inadequacy of the reductionist approach in discovering new therapeutic agents against complex diseases.

For more than 20 years, drug discovery has relied on two assumptions, i.e. (i) a therapeutic response can be triggered by modulating the activity of a single gene product, and (ii) a compound uncovered by its activity on a recombinant protein in vitro can perform its activity in vivo. Drug discovery operates accordingly by using the concepts of targets and pipelines. The target, such as a gene product, is the intended point of therapeutic intervention, and compounds that modulate its activity in vitro follow a series of downstream developments. This reductionist approach has developed due to advances in combinatorial chemistry, robotics, molecular biology, and genomics. The expectation of this approach is that the frequency of drug discovery will dramatically increase, while its associated cost would decrease. However, the frequency of new drug discovery has decreased, while the associated costs have surged. We performed a retrospective study that examined how successful development programs have led to marketed drugs for all indications except anti-infective and anti-neoplastic agents. We concluded that the target and pipeline paradigms are limited and are actually causing the drug development industry to collectively fail to meet the critical medical needs. Impact statement The initial scope of this investigation was to build the set of human genes that are presumed to be the therapeutic intervention points of US FDA-approved drugs, in all therapeutics areas but oncology. The prerequisite for this study was the establishment of the non-redundant set of all active pharmaceutical ingredients for these disease areas. Pertaining to complex diseases, the main observation was that there is not a single instance in the history of drug discovery, where a compound, initially selected by means of a biochemical assay, achieved a significant therapeutic response. The whole field of Drug R&D faces an unacceptable lack of new treatments to address unmet medical needs. The conclusion is that complex biological assays have to be designed for the primary selection of candidate therapeutics.

Each pregnancy linearly changes immune gene expression in the blood of healthy women compared with breast cancer patients

BackgroundThere is a large body of evidence demonstrating long-lasting protective effect of each full-term pregnancy (FTP) on the development of breast cancer (BC) later in life, a phenomenon that could be related to both hormonal and immunological changes during pregnancies. In this work, we studied the pregnancy-associated differences in peripheral blood gene expression profiles between healthy women and women diagnosed with BC in a prospective design.MethodsUsing an integrated system epidemiology approach, we modeled BC incidence as a function of parity in the Norwegian Women and Cancer (NOWAC) cohort (165,000 women) and then tested the resulting mathematical model using gene expression profiles in blood in a nested case–control study (460 invasive case–control pairs) of women from the NOWAC postgenome cohort. Lastly, we undertook a gene set enrichment analysis for immunological gene sets.ResultsA linear trend fitted the dataset precisely showing an 8% decrease in risk of BC for each FTP, independent of stratification on other risk factors and lasting for decades after a woman’s last FTP. Women with six children demonstrated 48% reduction in the incidence of BC compared to nulliparous. When we looked at gene expression, we found that 756 genes showed linear trends in cancer-free controls (false discovery rate [FDR] 5%), but this was not the case for any of the genes in BC cases. Gene set enrichment analysis of immunologic gene sets (C7 collection in Molecular Signatures Database) revealed 215 significantly enriched human gene sets (FDR 5%).ConclusionWe found marked differences in gene expression and enrichment profiles of immunologic gene sets between BC cases and healthy controls, suggesting an important protective effect of the immune system on BC risk.

Sensing the cilium, digital capture of ciliary data for comparative genomics investigations

BackgroundCilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.MethodsWe focused on the capture of information about cilia as studied in the laboratory mouse, a primary model of human biology. The workflow developed establishes a standard for capture of comparative functional data relevant to human biology. We established the 310 closest mouse orthologs of the 302 human genes defined in the SYSCILIA Gold Standard set of ciliary genes. For the mouse genes, we identified biomedical literature for curation and used Gene Ontology (GO) curation paradigms to provide functional annotations from these publications.ResultsEmploying a methodology for comprehensive capture of experimental data about cilia genes in structured, digital form, we established a workflow for curation of experimental literature detailing molecular function and roles of cilia proteins starting with the mouse orthologs of the human SYSCILIA gene set. We worked closely with the GO Consortium ontology development editors and the SYSCILIA Consortium to improve the representation of ciliary biology within the GO. During the time frame of the ontology improvement project, we have fully curated 134 of these 310 mouse genes, resulting in an increase in the number of ciliary and other experimental annotations.ConclusionsWe have improved the GO annotations available for mouse genes orthologous to the human genes in the SYSCILIA Consortium’s Gold Standard set. In addition, ciliary terminology in the GO itself was improved in collaboration with GO ontology developers and the SYSCILIA Consortium. These improvements to the GO terms for the functions and roles of ciliary proteins, along with the increase in annotations of the corresponding genes, enhance the representation of ciliary processes and localizations and improve access to these data during large-scale bioinformatic analyses.

Single-molecule, full-length transcript sequencing provides insight into the extreme metabolism of the ruby-throated hummingbird Archilochus colubris.

BackgroundHummingbirds oxidize ingested nectar sugars directly to fuel foraging but cannot sustain this fuel use during fasting periods, such as during the night or during long-distance migratory flights. Instead, fasting hummingbirds switch to oxidizing stored lipids that are derived from ingested sugars. The hummingbird liver plays a key role in moderating energy homeostasis and this remarkable capacity for fuel switching. Additionally, liver is the principle location of de novo lipogenesis, which can occur at exceptionally high rates, such as during premigratory fattening. Yet understanding how this tissue and whole organism moderates energy turnover is hampered by a lack of information regarding how relevant enzymes differ in sequence, expression, and regulation.FindingsWe generated a de novo transcriptome of the hummingbird liver using PacBio full-length cDNA sequencing (Iso-Seq), yielding 8.6Gb of sequencing data, or 2.6M reads from 4 different size fractions. We analyzed data using the SMRTAnalysis v3.1 Iso-Seq pipeline, then clustered isoforms into gene families to generate de novo gene contigs using Cogent. We performed orthology analysis to identify closely related sequences between our transcriptome and other avian and human gene sets. Finally, we closely examined homology of critical lipid metabolism genes between our transcriptome data and avian and human genomes.ConclusionsWe confirmed high levels of sequence divergence within hummingbird lipogenic enzymes, suggesting a high probability of adaptive divergent function in the hepatic lipogenic pathways. Our results leverage cutting-edge technology and a novel bioinformatics pipeline to provide a first direct look at the transcriptome of this incredible organism.

Easy and efficient ensemble gene set testing with EGSEA

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

Easy and efficient ensemble gene set testing with EGSEA.

Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots.Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation.EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.

BRAda: A Robust Method for Identification of Pre-microRNAs by Combining Adaboost Framework with BP and RF

Background: MicroRNAs (miRNAs) are a set of non-coding, short (approximately 21nt) RNAs that play an important role as a regulator in biological processes in the cells. The identification and discovery of pre-miRNAs are beneficial in understanding the regulatory process, the functions of miRNAs and other genes, and furthermore in biological evolution. Methods: Machine learning method has been a powerful technology in distinguishing the real premiRNAs from other hairpin-like sequences (pseudo pre-miRNAs). However, most of the commonly used classifiers are not promising in predicting performances on independent testing data sets. To overcome this, we proposed a novel BRAda algorithm integrating BP neural network and random forest classifier based on two balanced training sets. By distributing weights to these classifiers and the proposed 98-dimensional features, we obtained a strong classifier with high-accuracy and stability. Furthermore, based on the novel classifier we proposed, two independent testing sets (undated human and non-human pre-miRNAs) were employed to evaluate the prediction performance. Results: The novel method BRAda algorithm is significantly outperformed the other methods in identifying both human and non-human pre-miRNAs. Conclusion: The novel algorithm integrated BP neural network and random forest classifier based on two balanced training sets. Compared with other state-of-art machine-learning methods, the performance of BRAda was perfect (the ACC is over 99%) according to the validation. Besides, though the algorithm was trained by human gene sets, the prediction performance on non-human testing sets was also excellent (the average ACC is over 97%), which means the method not only has high stability but also robustness. By experiments and validation, the authors showed the method is an effective tool for pre-miRNA identification. Keywords: Biological process, BRAda, BP neural network, genes, Pre-miRNA identification, random forest.

Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens.

The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

Molecular signatures of class II HLA and p-16 status as an immune-based classification of OPSCC relying on known predictors of sensitivity to PD-1 blockade.

6052 Background: PD-1 inhibitors are known to have significant clinical activity in head and neck squamous cell cancer (SCCHN); there is, however, no selection criterion for SCCHN patients who may benefit from PD-1 inhibition. Utilizing RNA-seq analysis we explored a set of human genes encoding leukocyte antigens (HLAs) as part of a 37-gene panel predictive of response in melanoma patients to PD-1 inhibitors (Chen et al, Cancer Discov. 2016 Aug;6(8):827-37).We investigated whether this panel could define an immune-based classification of oropharyngeal squamous cell carcinoma (OPSCC). Methods: We have applied a minimal mutation and copy number content (151 genes) using an Agilent Clearseq DNA and an extensive Illumina Truseq RNA panel providing key information on gene fusions, differential gene expression, coding mutation and metagenomics on 47 SCCHN FFPE samples including 27 OPSCC. We performed an unsupervised hierarchical clustering of the samples. Two clusters with high and low expression were noted. Fisher’s exact test was performed to determine if the samples in each clusters were associated with p16 as a surrogate marker for HPV status. The same procedure was repeated on Level 3 transcriptome data from the TCGA via GDC data portal. Results: A set of fourteen immune related HLA antigen genes were identified within the 37-gene panel predictive to response to PD-1 inhibitors in p16+ versus - OPSCC (p = 0.015).We applied the same set of immune related HLA genes on the 103 patient samples from TCGA with known p16 status. When applied on all samples, there was no correlation between the HLA gene expression and p16 status (p = 0.1366); however, when restricted to OPSCC patients there was a high correlation with p16 status (p = 0.0047). Conclusions: We have identified a set of immune related HLA type II genes that are over-expressed in p16 positive OPSCC. This opens the door for further evaluation of these genes to better understand the immune related factors affecting the biology of HPV-associated OPSCC and its response to PD-1 inhibitors. (This research was supported by a grant NCI R21 CA182661-01A1to NFS and GZC).

Exploring potential biomarker responses to lithium in Daphnia magna from the perspectives of function and signaling networks

Intensive usage of electronic appliances containing lithium batteries causes an accumulation of e-trash. Environmental exposure to lithium batteries contaminates ecosystems. In air and water, the batteries form lithium hydroxide (LiOH) on their surfaces. LiOH enters the aquatic environment and contaminates the aquatic ecosystem by being absorbed into biological organisms. In this study, in order to identify meaningful potential biomarkers that appear in response to lithium, we measured significantly up- and down-regulated genes after LiOH exposure by conducting a microarray. In addition, we explored the functions of differentially expressed daphnia genes, and we conducted a comparative analysis in other species, Daphnia spp. to humans, then analyzed the signaling pathways using the human gene set derived from daphnia sequences that are differentially expressed in response to LiOH using the NCBI-BLAST tool and Pathway studio. As a result, we identified signaling pathways and suggested several potential biomarkers that are up- or down-regulated in response to lithium. This study may contribute to the development of a biomonitoring system which can detect the ecotoxicity of lithium. Furthermore, lithium toxicity in humans can be predicted, so the study may also provide potential biomarkers of lithium exposure in humans.

Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data

Large sets of candidate genes derived from high-throughput biological experiments can be characterized by functional enrichment analysis. The analysis consists of comparing the functions of one gene set against that of a background gene set. Then, functions related to a significant number of genes in the gene set are expected to be relevant. Web tools offering disease enrichment analysis on gene sets are often based on gene-disease associations from manually curated or experimental data that is accurate but does not cover all diseases discussed in the literature. Using associations automatically derived from literature data could be a cost effective method to improve the coverage of diseases for enrichment analysis at comparable levels of accuracy.
 We have implemented a method named Gene set to Diseases, GS2D, as a web tool performing disease enrichment analysis on human protein coding gene sets. It uses an automatically built dataset of more than 63 thousand gene-disease associations defined as statistically significant co-occurrences of genes and diseases in annotations of biomedical citations from PubMed. The dataset covers more diseases for enrichment analysis than the largest comparable curated database, Comparative Toxicogenomics Database, and its performance compared favourably to similar approaches based on manually curated or experimental data. Graphical and programmatic interfaces are available at http://cbdm.uni-mainz.de/geneset2diseases.

1557P - Identification of evolutionarily conserved DNA damage response (DDR) genes that alter sensitivity to cisplatin

Cisplatin is one of the most potent chemotherapeutic agents currently in use for clinical treatment of many types of cancer, including head and neck cancers (HNCs) and epithelial ovarian cancers (EOCs). Like other platinum agents, it is thought to function primarily by modifying DNA, forming intrastrand crosslinks and other DNA lesions. The molecular mechanisms of resistance to cisplatin are not fully understood, although some forms of resistance have been associated with changes in DNA Damage Response (DDR). Identification of the new therapeutic targets would both allow a better understanding of drug resistance mechanisms, and potentially provide new avenues for disease management. We have integrated data from a large number of functional screens performed in lower eukaryotes for genes conferring resistance to cisplatin and other DNA damaging agents such as g-ray-, X-ray-, and UV-radiation. We subsequently identified human orthologues of evolutionary conserved genes that are potentially involved in DDR and cisplatin sensitivity in humans. We then directly tested the role of some of these genes in response to the exposure to the DNA damaging agents in EOC and HNC cell lines. We used small interfering RNAs (siRNAs) to deplete either control genes or candidate genes of interest, and under basal and drug-treatment conditions, we assessed cell viability,the impact on phospho-H2AX focus formation, and activation of the ATR DDR kinase. Besides cisplatin, we assessed gene effect on two additional DNA damaging drugs, 5-fluorouracil and olaparib. We identified in silico a set of candidate human genes with yeast orthologs that are implicated in DDR and regulation of cisplatin sensitivity in Saccharomyces cerevisiae, developing a novel genomic resource. Depletion of a set of empirically tested genes sensitized human cancer cells to cisplatin and other DNA damaging drugs, and affected phospho-H2AX foci and DDR signaling. Our findings suggest that possible mechanism of sensitization to cisplatin caused by depletion of these genes involves reduced activation of DDR responses, and identifies new, non-canonical candidate regulators of DDR based on evolutionary analysis.

Size distribution of function-based human gene sets and the split–merge model

The sizes of paralogues—gene families produced by ancestral duplication—are known to follow a power-law distribution. We examine the size distribution of gene sets or gene families where genes are grouped by a similar function or share a common property. The size distribution of Human Gene Nomenclature Committee (HGNC) gene sets deviate from the power-law, and can be fitted much better by a beta rank function. We propose a simple mechanism to break a power-law size distribution by a combination of splitting and merging operations. The largest gene sets are split into two to account for the subfunctional categories, and a small proportion of other gene sets are merged into larger sets as new common themes might be realized. These operations are not uncommon for a curator of gene sets. A simulation shows that iteration of these operations changes the size distribution of Ensembl paralogues and could lead to a distribution fitted by a rank beta function. We further illustrate application of beta rank function by the example of distribution of transcription factors and drug target genes among HGNC gene families.

Musculocontractural Ehlers-Danlos syndrome and neurocristopathies: dermatan sulfate is required for Xenopus neural crest cells to migrate and adhere to fibronectin.

ABSTRACTOf all live births with congenital anomalies, approximately one-third exhibit deformities of the head and face. Most craniofacial disorders are associated with defects in a migratory stem and progenitor cell population, which is designated the neural crest (NC). Musculocontractural Ehlers–Danlos syndrome (MCEDS) is a heritable connective tissue disorder with distinct craniofacial features; this syndrome comprises multiple congenital malformations that are caused by dysfunction of dermatan sulfate (DS) biosynthetic enzymes, including DS epimerase-1 (DS-epi1; also known as DSE). Studies in mice have extended our understanding of DS-epi1 in connective tissue maintenance; however, its role in fetal development is not understood. We demonstrate that DS-epi1 is important for the generation of isolated iduronic acid residues in chondroitin sulfate (CS)/DS proteoglycans in early Xenopus embryos. The knockdown of DS-epi1 does not affect the formation of early NC progenitors; however, it impairs the correct activation of transcription factors involved in the epithelial–mesenchymal transition (EMT) and reduces the extent of NC cell migration, which leads to a decrease in NC-derived craniofacial skeleton, melanocytes and dorsal fin structures. Transplantation experiments demonstrate a tissue-autonomous role for DS-epi1 in cranial NC cell migration in vivo. Cranial NC explant and single-cell cultures indicate a requirement of DS-epi1 in cell adhesion, spreading and extension of polarized cell processes on fibronectin. Thus, our work indicates a functional link between DS and NC cell migration. We conclude that NC defects in the EMT and cell migration might account for the craniofacial anomalies and other congenital malformations in MCEDS, which might facilitate the diagnosis and development of therapies for this distressing condition. Moreover, the presented correlations between human DS-epi1 expression and gene sets of mesenchymal character, invasion and metastasis in neuroblastoma and malignant melanoma suggest an association between DS and NC-derived cancers.

Abstract PR03: High-resolution detection of fitness genes and genotype-specific cancer vulnerabilities with CRISPR-Cas9 screens

Abstract Genetic knockouts are a fundamental tool for elucidating gene function in model organisms and hold great potential for finding therapeutic targets for diseases such as cancer. The advance that pooled CRISPR-Cas9 library technology brings to human genetics sets the stage for identifying cellular fitness genes which operate either globally or specifically within a particular genetic background or environmental context. To extend the catalogue of human core and context-dependent fitness genes, we have developed the TKO (Toronto KnockOut) library, a second-generation gRNA library of 176,500 guides targeting 17,661 human protein coding genes. We used the library to screen five human cell lines, representing a cross-section of wild type and cancer tissues, to identify genes whose knockouts induced significant fitness defects. We consistently discover fivefold more fitness genes than were previously observed using systematic RNA interference, including many genes at moderate expression levels that are largely refractory to RNAi methods. We expand the known set of human core fitness genes more than fourfold to 1,580 genes, and identify dozens of essential protein complexes, both known and novel, whose heterozygous copy loss in chromosomally unstable cancers may induce a therapeutic window. We further characterize novel fitness genes of unknown function and find that they all likely exist in protein complexes with other essential genes. TKO screens accurately recapitulate pathway-specific genetic vulnerabilities induced by known oncogenes and reveal cell-type-specific dependencies for specific receptor tyrosine kinases, even in oncogenic KRAS backgrounds. We also identified a surprising and specific dependency on mitochondrial activity, which strongly supports the idea that oxidative phosphorylation dependency - a clear exception to the Warburg effect - is a targetable weakness of some tumors. Our findings demonstrate that the CRISPR-Cas9 system fundamentally alters the landscape for systematic genetics in human cells through rigorous identification of cell line essential genes, affording a high-resolution view of the genetic vulnerabilities of a cell that may represent therapeutic opportunities in cancer and that might conceivably contribute to cell plasticity and tumor progression. Citation Format: Traver Hart, Megha Chandrashekhar, Michael Aregger, Zachary Steinhart, Kevin R. Brown, Graham Macleod, Monika Mis, Michal Zimmermann, Amelie Fradet-Turcotte, Song Sun, Peter Driks, Sachdev Sidhu, Frederick P. Roth, Olivia S. Rissland, Daniel Durocher, Stephane Angers, Jason Moffat. High-resolution detection of fitness genes and genotype-specific cancer vulnerabilities with CRISPR-Cas9 screens. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr PR03.

Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).

Success in extracting biological relationships is mainly dependent on the complexity of the task as well as the availability of high-quality training data. Here, we describe the new corpora in the systems biology modeling language BEL for training and testing biological relationship extraction systems that we prepared for the BioCreative V BEL track. BEL was designed to capture relationships not only between proteins or chemicals, but also complex events such as biological processes or disease states. A BEL nanopub is the smallest unit of information and represents a biological relationship with its provenance. In BEL relationships (called BEL statements), the entities are normalized to defined namespaces mainly derived from public repositories, such as sequence databases, MeSH or publicly available ontologies. In the BEL nanopubs, the BEL statements are associated with citation information and supportive evidence such as a text excerpt. To enable the training of extraction tools, we prepared BEL resources and made them available to the community. We selected a subset of these resources focusing on a reduced set of namespaces, namely, human and mouse genes, ChEBI chemicals, MeSH diseases and GO biological processes, as well as relationship types ‘increases’ and ‘decreases’. The published training corpus contains 11 000 BEL statements from over 6000 supportive text excerpts. For method evaluation, we selected and re-annotated two smaller subcorpora containing 100 text excerpts. For this re-annotation, the inter-annotator agreement was measured by the BEL track evaluation environment and resulted in a maximal F-score of 91.18% for full statement agreement. In addition, for a set of 100 BEL statements, we do not only provide the gold standard expert annotations, but also text excerpts pre-selected by two automated systems. Those text excerpts were evaluated and manually annotated as true or false supportive in the course of the BioCreative V BEL track task.Database URL: http://wiki.openbel.org/display/BIOC/Datasets

The Ensembl gene annotation system.

The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html

LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data.

BackgroundThe GENCODE project has collected over 10,000 human long non-coding RNA (lncRNA) genes. However, the vast majority of them remain to be functionally characterized. Computational investigation of potential functions of human lncRNA genes is helpful to guide further experimental studies on lncRNAs.ResultsIn this study, based on expression correlation between lncRNAs and protein-coding genes across 19 human normal tissues, we used the hypergeometric test to functionally annotate a single lncRNA or a set of lncRNAs with significantly enriched functional terms among the protein-coding genes that are significantly co-expressed with the lncRNA(s). The functional terms include all nodes in the Gene Ontology (GO) and 4,380 human biological pathways collected from 12 pathway databases. We successfully mapped 9,625 human lncRNA genes to GO terms and biological pathways, and then developed the first ontology-driven user-friendly web interface named lncRNA2Function, which enables researchers to browse the lncRNAs associated with a specific functional term, the functional terms associated with a specific lncRNA, or to assign functional terms to a set of human lncRNA genes, such as a cluster of co-expressed lncRNAs. The lncRNA2Function is freely available at http://mlg.hit.edu.cn/lncrna2function.ConclusionsThe LncRNA2Function is an important resource for further investigating the functions of a single human lncRNA, or functionally annotating a set of human lncRNAs of interest.