Journal articles: 'Bioinformatics. eng'

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Guang, Ma. "In Silicon Cloning and Bioinformatics Analysis of the Raphanus Sativus WUS Gene." Engineering 05, no. 10 (2013): 509–12. http://dx.doi.org/10.4236/eng.2013.510b104.

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Lozano-Muniz, Susana, and Maria del Carmen Urzua-Hernandez. "Structural Bioinformatics of Protein & DNA, as Early Stimulation in Basic Education of Rural and Indigenous Communities of Oaxaca." Engineering 05, no. 10 (2013): 255–58. http://dx.doi.org/10.4236/eng.2013.510b053.

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Wang, Xinyu, Jiaojiao Yang, and Xueren Gao. "Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis." Science Progress 104, no. 1 (2021): 003685042199727. http://dx.doi.org/10.1177/0036850421997276.

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Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

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Listik, Eduardo, Ben Horst, Alex Seok Choi, Nam Y. Lee, Balázs Győrffy, and Karthikeyan Mythreye. "A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors." PLOS ONE 16, no. 4 (2021): e0249558. http://dx.doi.org/10.1371/journal.pone.0249558.

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Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

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Eun, Jung Woo, Chul Won Seo, Geum Ok Baek, et al. "Circulating Exosomal MicroRNA-1307-5p as a Predictor for Metastasis in Patients with Hepatocellular Carcinoma." Cancers 12, no. 12 (2020): 3819. http://dx.doi.org/10.3390/cancers12123819.

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Exosomal microRNAs (exo-miRs) contribute to cancer metastasis. To identify pro-metastatic circulating exo-miRs in hepatocellular carcinoma (HCC), next-generation sequencing-based plasma exo-miR profiles of 14 patients with HCC (eight non-metastatic and six with metastasis within 1 year of follow-up) were analyzed. Sixty-one miRs were significantly overexpressed among patients with metastatic HCC. Candidate miRs were selected through integrative analyses of two different public expression datasets, GSE67140 and The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA_LIHC). Integrative analyses revealed 3 of 61 miRs (miR-106b-5p, miR-1307-5p, and miR-340-5p) commonly overexpressed both in metastasis and vascular invasion groups, with prognostic implications. Validation was performed using stored blood samples of 150 patients with HCC. Validation analysis showed that circulating exo-miR-1307-5p was significantly overexpressed in the metastasis group (p = 0.04), as well as in the vascular invasion and tumor recurrence groups. Circulating exo-miR-1307-5p expression was significantly correlated with tumor stage progression (p < 0.0001). Downstream signaling pathways of miR-1307 were predicted using TargetScan and Ingenuity Pathway Analysis. On comprehensive bioinformatics analysis, the downstream pathway of miR-1307-5p, promoting epithelial–mesenchymal transition (EMT), showed SEC14L2 and ENG downregulation. Our results show that circulating exo-miR-1307-5p promotes metastasis and helps predict metastasis in HCC, and SEC14L2 and ENG are target tumor suppressor genes of miR-1307 that promote EMT.

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Suh, K. Stephen, Sreeja Sarojini, Maher Youssif, et al. "Tissue Banking, Bioinformatics, and Electronic Medical Records: The Front-End Requirements for Personalized Medicine." Journal of Oncology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/368751.

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Personalized medicine promises patient-tailored treatments that enhance patient care and decrease overall treatment costs by focusing on genetics and “-omics” data obtained from patient biospecimens and records to guide therapy choices that generate good clinical outcomes. The approach relies on diagnostic and prognostic use of novel biomarkers discovered through combinations of tissue banking, bioinformatics, and electronic medical records (EMRs). The analytical power of bioinformatic platforms combined with patient clinical data from EMRs can reveal potential biomarkers and clinical phenotypes that allow researchers to develop experimental strategies using selected patient biospecimens stored in tissue banks. For cancer, high-quality biospecimens collected at diagnosis, first relapse, and various treatment stages provide crucial resources for study designs. To enlarge biospecimen collections, patient education regarding the value of specimen donation is vital. One approach for increasing consent is to offer publically available illustrations and game-like engagements demonstrating how wider sample availability facilitates development of novel therapies. The critical value of tissue bank samples, bioinformatics, and EMR in the early stages of the biomarker discovery process for personalized medicine is often overlooked. The data obtained also require cross-disciplinary collaborations to translate experimental results into clinical practice and diagnostic and prognostic use in personalized medicine.

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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, et al. "Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers." Journal of Clinical Oncology 35, no. 15_suppl (2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.

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Saygin, Didem, Tracy Tabib, Humberto E. T. Bittar, et al. "Transcriptional profiling of lung cell populations in idiopathic pulmonary arterial hypertension." Pulmonary Circulation 10, no. 1 (2020): ??? http://dx.doi.org/10.1177/2045894020908782.

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Despite recent improvements in management of idiopathic pulmonary arterial hypertension, mortality remains high. Understanding the alterations in the transcriptome–phenotype of the key lung cells involved could provide insight into the drivers of pathogenesis. In this study, we examined differential gene expression of cell types implicated in idiopathic pulmonary arterial hypertension from lung explants of patients with idiopathic pulmonary arterial hypertension compared to control lungs. After tissue digestion, we analyzed all cells from three idiopathic pulmonary arterial hypertension and six control lungs using droplet-based single cell RNA-sequencing. After dimensional reduction by t-stochastic neighbor embedding, we compared the transcriptomes of endothelial cells, pericyte/smooth muscle cells, fibroblasts, and macrophage clusters, examining differential gene expression and pathways implicated by analysis of Gene Ontology Enrichment. We found that endothelial cells and pericyte/smooth muscle cells had the most differentially expressed gene profile compared to other cell types. Top differentially upregulated genes in endothelial cells included novel genes: ROBO4, APCDD1, NDST1, MMRN2, NOTCH4, and DOCK6, as well as previously reported genes: ENG, ORAI2, TFDP1, KDR, AMOTL2, PDGFB, FGFR1, EDN1, and NOTCH1. Several transcription factors were also found to be upregulated in idiopathic pulmonary arterial hypertension endothelial cells including SOX18, STRA13, LYL1, and ELK, which have known roles in regulating endothelial cell phenotype. In particular, SOX18 was implicated through bioinformatics analyses in regulating the idiopathic pulmonary arterial hypertension endothelial cell transcriptome. Furthermore, idiopathic pulmonary arterial hypertension endothelial cells upregulated expression of FAM60A and HDAC7, potentially affecting epigenetic changes in idiopathic pulmonary arterial hypertension endothelial cells. Pericyte/smooth muscle cells expressed genes implicated in regulation of cellular apoptosis and extracellular matrix organization, and several ligands for genes showing increased expression in endothelial cells. In conclusion, our study represents the first detailed look at the transcriptomic landscape across idiopathic pulmonary arterial hypertension lung cells and provides robust insight into alterations that occur in vivo in idiopathic pulmonary arterial hypertension lungs.

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Xu, Jingjing, Bin Chen, Zhengbo He, and Youjin Hao. "Cloning, Characterization and Bioinformatic Analysis of the Gene Encoding the Larval Serum Protein 2 in Diapause of the Onion Maggot, Delia Antiqua." Engineering 05, no. 10 (2013): 487–90. http://dx.doi.org/10.4236/eng.2013.510b100.

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Gruenstaeudl, Michael. "annonex2embl: automatic preparation of annotated DNA sequences for bulk submissions to ENA." Bioinformatics 36, no. 12 (2020): 3841–48. http://dx.doi.org/10.1093/bioinformatics/btaa209.

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Abstract Motivation The submission of annotated sequence data to public sequence databases constitutes a central pillar in biological research. The surge of novel DNA sequences awaiting database submission due to the application of next-generation sequencing has increased the need for software tools that facilitate bulk submissions. This need has yet to be met with the concurrent development of tools to automate the preparatory work preceding such submissions. Results The author introduce annonex2embl, a Python package that automates the preparation of complete sequence flatfiles for large-scale sequence submissions to the European Nucleotide Archive. The tool enables the conversion of DNA sequence alignments that are co-supplied with sequence annotations and metadata to submission-ready flatfiles. Among other features, the software automatically accounts for length differences among the input sequences while maintaining correct annotations, automatically interlaces metadata to each record and displays a design suitable for easy integration into bioinformatic workflows. As proof of its utility, annonex2embl is employed in preparing a dataset of more than 1500 fungal DNA sequences for database submission. Availability and implementation annonex2embl is freely available via the Python package index at http://pypi.python.org/pypi/annonex2embl. Supplementary information Supplementary data are available at Bioinformatics online.

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ADAK, SUDESHNA. "e2eXpress: END-TO-END BIOINFORMATICS AND KNOWLEDGE MANAGEMENT SYSTEM FOR MICROARRAYS." Journal of Biological Systems 10, no. 04 (2002): 285–302. http://dx.doi.org/10.1142/s0218339002000664.

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The advent of high-density microarrays has made it possible for scientists to measure the expression levels of thousands of genes simultaneously. Understanding and interpreting the massive volumes of microarray data is necessary to unravel the molecular basis of diseases and will someday lead to medicines tailored for individual genetic profiles. One of the main barriers to realizing the full potential of microarrays today is the need for specialized bioinformatics and knowledge management solutions required to mine the microarray data for biological information. After initial efforts at clustering expression data based on similarity, scientists have recognized the need to cross-reference and correlate experimental data with external data sources, to improve the quality of the biological conclusions that can be drawn. This paper describes e2eXpress, such an end-to-end Bioinformatics and Knowledge Management System for Microarrays. e2eXpress incorporates basic data management and analysis tasks with novel approaches for mining various molecular biological databases to summarize information regarding coregulated gene clusters. In particular, this paper describes two new algorithms: (a) Text Mining for Gene Clusters: a statistical algorithm that is aimed at deriving biologically relevant information for gene clusters from the biomedical literature; (b) Pathway Scoring for Gene Clusters: a computational algorithm that is aimed at deriving pathway related information for gene clusters. This paper describes the variety of statistical and computational algorithms that are required to mine the transcriptome in conjunction with extraneous data sources that can lead to real biological advances.

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Jafarpour, Ali, Simon Gregersen, Rocio Marciel Gomes, et al. "Biofunctionality of Enzymatically Derived Peptides from Codfish (Gadus morhua) Frame: Bulk In Vitro Properties, Quantitative Proteomics, and Bioinformatic Prediction." Marine Drugs 18, no. 12 (2020): 599. http://dx.doi.org/10.3390/md18120599.

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Protein hydrolysates show great promise as bioactive food and feed ingredients and for valorization of side-streams from e.g., the fish processing industry. We present a novel approach for hydrolysate characterization that utilizes proteomics data for calculation of weighted mean peptide properties (length, molecular weight, and charge) and peptide-level abundance estimation. Using a novel bioinformatic approach for subsequent prediction of biofunctional properties of identified peptides, we are able to provide an unprecedented, in-depth characterization. The study further characterizes bulk emulsifying, foaming, and in vitro antioxidative properties of enzymatic hydrolysates derived from cod frame by application of Alcalase and Neutrase, individually and sequentially, as well as the influence of heat pre-treatment. All hydrolysates displayed comparable or higher emulsifying activity and stability than sodium caseinate. Heat-treatment significantly increased stability but showed a negative effect on the activity and degree of hydrolysis. Lower degrees of hydrolysis resulted in significantly higher chelating activity, while the opposite was observed for radical scavenging activity. Combining peptide abundance with bioinformatic prediction, we identified several peptides that are likely linked to the observed differences in bulk emulsifying properties. The study highlights the prospects of applying proteomics and bioinformatics for hydrolysate characterization and in food protein science.

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Trieu, Hai-Long, Thy Thy Tran, Khoa N. A. Duong, Anh Nguyen, Makoto Miwa, and Sophia Ananiadou. "DeepEventMine: end-to-end neural nested event extraction from biomedical texts." Bioinformatics 36, no. 19 (2020): 4910–17. http://dx.doi.org/10.1093/bioinformatics/btaa540.

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Abstract Motivation Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools. Results We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance. Availability and implementation Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine. Supplementary information Supplementary data are available at Bioinformatics online.

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Moriyama, Takuya, Seiya Imoto, Shuto Hayashi, Yuichi Shiraishi, Satoru Miyano, and Rui Yamaguchi. "A Bayesian model integration for mutation calling through data partitioning." Bioinformatics 35, no. 21 (2019): 4247–54. http://dx.doi.org/10.1093/bioinformatics/btz233.

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Abstract Motivation Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. Results We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. Availability and implementation https://github.com/takumorizo/OHVarfinDer. Supplementary information Supplementary data are available at Bioinformatics online.

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Abyzov, A., M. Errami, C. M. Leslin, and V. A. Ilyin. "Friend, an integrated analytical front-end application for bioinformatics." Bioinformatics 21, no. 18 (2005): 3677–78. http://dx.doi.org/10.1093/bioinformatics/bti602.

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Polaka, Inese, Igor Tom, and Arkady Borisov. "Decision Tree Classifiers in Bioinformatics." Scientific Journal of Riga Technical University. Computer Sciences 42, no. 1 (2010): 118–23. http://dx.doi.org/10.2478/v10143-010-0052-4.

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Decision Tree Classifiers in BioinformaticsThis paper presents a literature review of articles related to the use of decision tree classifiers in gene microarray data analysis published in the last ten years. The main focus is on researches solving the cancer classification problem using single decision tree classifiers (algorithms C4.5 and CART) and decision tree forests (e.g. random forests) showing strengths and weaknesses of the proposed methodologies when compared to other popular classification methods. The article also touches the use of decision tree classifiers in gene selection.

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Schaffer, A. A., E. S. Rice, W. Cook, and R. Agarwala. "rh_tsp_map 3.0: end-to-end radiation hybrid mapping with improved speed and quality control." Bioinformatics 23, no. 9 (2007): 1156–58. http://dx.doi.org/10.1093/bioinformatics/btm077.

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Georgiev, Ivelin, and Bruce R. Donald. "Dead-End Elimination with Backbone Flexibility." Bioinformatics 23, no. 13 (2007): i185—i194. http://dx.doi.org/10.1093/bioinformatics/btm197.

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Tsubaki, Masashi, Kentaro Tomii, and Jun Sese. "Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences." Bioinformatics 35, no. 2 (2018): 309–18. http://dx.doi.org/10.1093/bioinformatics/bty535.

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Phuong, Ho Thi Bich, Vien Ngoc Thach, Luong Hoang Ngan, and Le Thi Truc Linh. "Using Bioinformatics to predict potential targets of Microrna-144 in osteoarthritis." ENGINEERING AND TECHNOLOGY 8, no. 1 (2020): 43–52. http://dx.doi.org/10.46223/hcmcoujs.tech.en.8.1.335.2018.

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MicroRNAs are short endogenous non-coding RNA molecules, typically 19-25 nucleotides in length, which negatively regulate gene expression through binding to 3’UTR of target mRNAs, leading to repression of protein translation or target mRNA degradation. MicroRNA-144 (miR-144) was found as an abnormal expression in various diseases, including osteoarthritis (OA). We have identified increased microRNA-144 expression in early phase and end stage of OA. However, the molecular mechanism of this increase has not been yet to be determined yet. Using bioinformatics tools, we found more than 4,000 mRNAs that are predicted to be potential direct targets of miR-144, including mRNAs involved in the critical signaling pathways in OA e.g. TGFβ/Smad2/3 and WNT/β-catenin. Results from this research provide information for future ex periments to validate miR-144 potential targets.

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Bradnam, Keith, and Sean May. "The UK Crop Plant Bioinformatics Network (UK CropNet)." Yeast 1, no. 4 (2000): 335–38. http://dx.doi.org/10.1155/2000/495018.

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UK CropNet currently provides a range of databases (and database-mining tools) to the plant community that are all freely accessible through our website (http://ukcrop.net/). Recent upgrades have meant that we can now expand the range of available facilities (e.g. addition of new databases) whilst also strengthening and improving access to existing services (e.g. providing a BLAST search facility against sequences in our databases). This article will briefly outline these and other new developments in our service.

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Long, Q., D. MacArthur, Z. Ning, and C. Tyler-Smith. "HI: haplotype improver using paired-end short reads." Bioinformatics 25, no. 18 (2009): 2436–37. http://dx.doi.org/10.1093/bioinformatics/btp412.

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Hu, Xuesong, Jianying Yuan, Yujian Shi, et al. "pIRS: Profile-based Illumina pair-end reads simulator." Bioinformatics 28, no. 11 (2012): 1533–35. http://dx.doi.org/10.1093/bioinformatics/bts187.

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Crane, C. F., and Y. M. Crane. "A nearest-neighboring-end algorithm for genetic mapping." Bioinformatics 21, no. 8 (2004): 1579–91. http://dx.doi.org/10.1093/bioinformatics/bti164.

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Li, W., C. J. Rehmeyer, C. Staben, and M. L. Farman. "TERMINUS--Telomeric End-Read Mining IN Unassembled Sequences." Bioinformatics 21, no. 8 (2004): 1695–98. http://dx.doi.org/10.1093/bioinformatics/bti181.

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V.Lukashin, A., and J. J.Rosa. "Local multiple sequence alignment using dead-end elimination." Bioinformatics 15, no. 11 (1999): 947–53. http://dx.doi.org/10.1093/bioinformatics/15.11.947.

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Han, Renmin, Yu Li, Xin Gao, and Sheng Wang. "An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing." Bioinformatics 34, no. 17 (2018): i722—i731. http://dx.doi.org/10.1093/bioinformatics/bty555.

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Pal, Soumitra, and Teresa M. Przytycka. "Bioinformatics pipeline using JUDI: Just Do It!" Bioinformatics 36, no. 8 (2019): 2572–74. http://dx.doi.org/10.1093/bioinformatics/btz956.

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Abstract Summary Large-scale data analysis in bioinformatics requires pipelined execution of multiple software. Generally each stage in a pipeline takes considerable computing resources and several workflow management systems (WMS), e.g. Snakemake, Nextflow, Common Workflow Language, Galaxy, etc. have been developed to ensure optimum execution of the stages across two invocations of the pipeline. However, when the pipeline needs to be executed with different settings of parameters, e.g. thresholds, underlying algorithms, etc. these WMS require significant scripting to ensure an optimal execution. We developed JUDI on top of DoIt, a Python based WMS, to systematically handle parameter settings based on the principles of database management systems. Using a novel modular approach that encapsulates a parameter database in each task and file associated with a pipeline stage, JUDI simplifies plug-and-play of the pipeline stages. For a typical pipeline with n parameters, JUDI reduces the number of lines of scripting required by a factor of O(n). With properly designed parameter databases, JUDI not only enables reproducing research under published values of parameters but also facilitates exploring newer results under novel parameter settings. Availability and implementation https://github.com/ncbi/JUDI Supplementary information Supplementary data are available at Bioinformatics online.

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Ahram, Mamoun, and Emanuel F. Petricoin. "Proteomics Discovery of Disease Biomarkers." Biomarker Insights 3 (January 2008): BMI.S689. http://dx.doi.org/10.4137/bmi.s689.

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Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics.

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Quackenbush, J., and S. L. Salzberg. "It is time to end the patenting of software." Bioinformatics 22, no. 12 (2006): 1416–17. http://dx.doi.org/10.1093/bioinformatics/btl167.

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Lopes, Robson da Silva, Nathalia Maria Resende, Adenilda Cristina Honorio-França, and Eduardo Luzía França. "Application of Bioinformatics in Chronobiology Research." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/153839.

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Bioinformatics and other well-established sciences, such as molecular biology, genetics, and biochemistry, provide a scientific approach for the analysis of data generated through “omics” projects that may be used in studies of chronobiology. The results of studies that apply these techniques demonstrate how they significantly aided the understanding of chronobiology. However, bioinformatics tools alone cannot eliminate the need for an understanding of the field of research or the data to be considered, nor can such tools replace analysts and researchers. It is often necessary to conduct an evaluation of the results of a data mining effort to determine the degree of reliability. To this end, familiarity with the field of investigation is necessary. It is evident that the knowledge that has been accumulated through chronobiology and the use of tools derived from bioinformatics has contributed to the recognition and understanding of the patterns and biological rhythms found in living organisms. The current work aims to develop new and important applications in the near future through chronobiology research.

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Zhang, J., K. Kobert, T. Flouri, and A. Stamatakis. "PEAR: a fast and accurate Illumina Paired-End reAd mergeR." Bioinformatics 30, no. 5 (2013): 614–20. http://dx.doi.org/10.1093/bioinformatics/btt593.

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Hu, Yin, Kai Wang, Xiaping He, Derek Y. Chiang, Jan F. Prins, and Jinze Liu. "A probabilistic framework for aligning paired-end RNA-seq data." Bioinformatics 26, no. 16 (2010): 1950–57. http://dx.doi.org/10.1093/bioinformatics/btq336.

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Ariyaratne, Pramila Nuwantha, and Wing-Kin Sung. "PE-Assembler: de novo assembler using short paired-end reads." Bioinformatics 27, no. 2 (2010): 167–74. http://dx.doi.org/10.1093/bioinformatics/btq626.

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Miladi, Milad, Soheila Montaseri, Rolf Backofen, and Martin Raden. "Integration of accessibility data from structure probing into RNA–RNA interaction prediction." Bioinformatics 35, no. 16 (2018): 2862–64. http://dx.doi.org/10.1093/bioinformatics/bty1029.

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Abstract Summary Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA–RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA–RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA–RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. Availability and implementation https://github.com/BackofenLab/IntaRNA Supplementary information Supplementary data are available at Bioinformatics online.

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Li, Yang, Jeremy Chien, David I. Smith, and Jian Ma. "FusionHunter: identifying fusion transcripts in cancer using paired-end RNA-seq." Bioinformatics 27, no. 12 (2011): 1708–10. http://dx.doi.org/10.1093/bioinformatics/btr265.

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Muggli, Martin D., Simon J. Puglisi, Roy Ronen, and Christina Boucher. "Misassembly detection using paired-end sequence reads and optical mapping data." Bioinformatics 31, no. 12 (2015): i80—i88. http://dx.doi.org/10.1093/bioinformatics/btv262.

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Munro, Jacob E., Sally L. Dunwoodie, and Eleni Giannoulatou. "SVPV: a structural variant prediction viewer for paired-end sequencing datasets." Bioinformatics 33, no. 13 (2017): 2032–33. http://dx.doi.org/10.1093/bioinformatics/btx117.

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39

Counsell, Damian. "Meeting Review: 2002 O'Reilly Bioinformatics Technology Conference." Comparative and Functional Genomics 3, no. 3 (2002): 264–69. http://dx.doi.org/10.1002/cfg.170.

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At the end of January I travelled to the States to speak at and attend the first O’Reilly Bioinformatics Technology Conference [14]. It was a large, well-organized and diverse meeting with an interesting history. Although the meeting was not a typical academic conference, its style will, I am sure, become more typical of meetings in both biological and computational sciences.Speakers at the event included prominent bioinformatics researchers such as Ewan Birney, Terry Gaasterland and Lincoln Stein; authors and leaders in the open source programming community like Damian Conway and Nat Torkington; and representatives from several publishing companies including the Nature Publishing Group, Current Science Group and the President of O’Reilly himself, Tim O’Reilly. There were presentations, tutorials, debates, quizzes and even a ‘jam session’ for musical bioinformaticists.

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Rausch, T., T. Zichner, A. Schlattl, A. M. Stutz, V. Benes, and J. O. Korbel. "DELLY: structural variant discovery by integrated paired-end and split-read analysis." Bioinformatics 28, no. 18 (2012): i333—i339. http://dx.doi.org/10.1093/bioinformatics/bts378.

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Benelli, Matteo, Chiara Pescucci, Giuseppina Marseglia, Marco Severgnini, Francesca Torricelli, and Alberto Magi. "Discovering chimeric transcripts in paired-end RNA-seq data by using EricScript." Bioinformatics 28, no. 24 (2012): 3232–39. http://dx.doi.org/10.1093/bioinformatics/bts617.

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42

Kim, Jin-Dong, Yue Wang, Toyofumi Fujiwara, Shujiro Okuda, Tiffany J. Callahan, and K. Bretonnel Cohen. "Open Agile text mining for bioinformatics: the PubAnnotation ecosystem." Bioinformatics 35, no. 21 (2019): 4372–80. http://dx.doi.org/10.1093/bioinformatics/btz227.

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Abstract Motivation Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. Results This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. Availability and implementation The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.

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Robinson, Welles, Roded Sharan, and Mark D. M. Leiserson. "Modeling clinical and molecular covariates of mutational process activity in cancer." Bioinformatics 35, no. 14 (2019): i492—i500. http://dx.doi.org/10.1093/bioinformatics/btz340.

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Abstract Motivation Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes). Results To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor’s observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort—using cancer type as a covariate—and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas. Availability and implementation TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper. Supplementary information Supplementary data are available at Bioinformatics online.

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Bradbury, Alice, Rachel O’Donnell, Yvette Drew, Nicola J. Curtin, and Sweta Sharma Saha. "Characterisation of Ovarian Cancer Cell Line NIH-OVCAR3 and Implications of Genomic, Transcriptomic, Proteomic and Functional DNA Damage Response Biomarkers for Therapeutic Targeting." Cancers 12, no. 7 (2020): 1939. http://dx.doi.org/10.3390/cancers12071939.

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In order to be effective models to identify biomarkers of chemotherapy response, cancer cell lines require thorough characterization. In this study, we characterised the widely used high grade serous ovarian cancer (HGSOC) cell line NIH-OVCAR3 using bioinformatics, cytotoxicity assays and molecular/functional analyses of DNA damage response (DDR) pathways in comparison to an ovarian cancer cell line panel. Bioinformatic analysis confirmed the HGSOC-like features of NIH-OVCAR3, including low mutation frequency, TP53 loss and high copy number alteration frequency similar to 201 HGSOCs analysed (TCGA). Cytotoxicity assays were performed for the standard of care chemotherapy, carboplatin, and DDR targeting drugs: rucaparib (a PARP inhibitor) and VE-821 (an ATR inhibitor). Interestingly, NIH-OVCAR3 cells showed sensitivity to carboplatin and rucaparib which was explained by functional loss of homologous recombination repair (HRR) identified by plasmid re-joining assay, despite the ability to form RAD51 foci and absence of mutations in HRR genes. NIH-OVCAR3 cells also showed high non-homologous end joining activity, which may contribute to HRR loss and along with genomic amplification in ATR and TOPBP1, could explain the resistance to VE-821. In summary, NIH-OVCAR3 cells highlight the complexity of HGSOCs and that genomic or functional characterization alone might not be enough to predict/explain chemotherapy response.

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Coy, Samantha, Eric Gann, Helena Pound, Steven Short, and Steven Wilhelm. "Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions." Viruses 10, no. 9 (2018): 487. http://dx.doi.org/10.3390/v10090487.

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The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits (e.g., conserved core genes) of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.

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Guo, Yu Qing, Jian Jin, and Ai Yuan Liu. "Comparison Study on Bioinformatics Research Papers Written by Chinese and Worldwide Authors Respectively." Advanced Materials Research 282-283 (July 2011): 421–24. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.421.

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From 1997 to 2009, altogether 2853 papers from 48059 papers extracted in SCI-Expended on bioinformatics could be attributed to Chinese scholars in the world. This study examined the top 30 SCI-E journals that published the largest number of papers on bioinformatics written by authors worldwide and by Chinese authors respectively, and measured collaborations amongst country and the productivity distribution of relevant institutions in the world, the languages in which documents were published, the mode of publication (e.g. article, reviews and proceedings paper etc.). This study will provide general insights of the characteristics of bioinformatics research papers written by Chinese authors.

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Hajirasouliha, Iman, Fereydoun Hormozdiari, Can Alkan, et al. "Detection and characterization of novel sequence insertions using paired-end next-generation sequencing." Bioinformatics 26, no. 10 (2010): 1277–83. http://dx.doi.org/10.1093/bioinformatics/btq152.

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Liu, Hui, and Tingjun Hou. "CaFE: a tool for binding affinity prediction using end-point free energy methods." Bioinformatics 32, no. 14 (2016): 2216–18. http://dx.doi.org/10.1093/bioinformatics/btw215.

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Chen, Jiao, Yingchao Zhao, and Yanni Sun. "De novo haplotype reconstruction in viral quasispecies using paired-end read guided path finding." Bioinformatics 34, no. 17 (2018): 2927–35. http://dx.doi.org/10.1093/bioinformatics/bty202.

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Willing, Eva-Maria, Margarete Hoffmann, Juliane D. Klein, Detlef Weigel, and Christine Dreyer. "Paired-end RAD-seq for de novo assembly and marker design without available reference." Bioinformatics 27, no. 16 (2011): 2187–93. http://dx.doi.org/10.1093/bioinformatics/btr346.

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Journal articles on the topic 'Bioinformatics. eng'

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