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Journal articles on the topic 'LINCS bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Wang, Zichen, Neil R. Clark, and Avi Ma’ayan. "Drug-induced adverse events prediction with the LINCS L1000 data." Bioinformatics 32, no. 15 (2016): 2338–45. http://dx.doi.org/10.1093/bioinformatics/btw168.

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2

Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.

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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability and implementation Slinky is freely available as an R package at http://bioconductor.org/packages/slinky

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3

Qiu, Yue, Tianhuan Lu, Hansaim Lim, and Lei Xie. "A Bayesian approach to accurate and robust signature detection on LINCS L1000 data." Bioinformatics 36, no. 9 (2020): 2787–95. http://dx.doi.org/10.1093/bioinformatics/btaa064.

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Abstract Motivation LINCS L1000 dataset contains numerous cellular expression data induced by large sets of perturbagens. Although it provides invaluable resources for drug discovery as well as understanding of disease mechanisms, the existing peak deconvolution algorithms cannot recover the accurate expression level of genes in many cases, inducing severe noise in the dataset and limiting its applications in biomedical studies. Results Here, we present a novel Bayesian-based peak deconvolution algorithm that gives unbiased likelihood estimations for peak locations and characterize the peaks with probability based z-scores. Based on the above algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent the features of perturbagen. The performance of the proposed pipeline is evaluated using similarity between the signatures of bio-replicates and the drugs with shared targets, and the results show that signatures derived from our pipeline gives a substantially more reliable and informative representation for perturbagens than existing methods. Thus, the new pipeline may significantly boost the performance of L1000 data in the downstream applications such as drug repurposing, disease modeling and gene function prediction. Availability and implementation The code and the precomputed data for LINCS L1000 Phase II (GSE 70138) are available at https://github.com/njpipeorgan/L1000-bayesian. Supplementary information Supplementary data are available at Bioinformatics online.

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4

Wakai, Eri, Yuya Suzumura, Kenji Ikemura, et al. "An Integrated In Silico and In Vivo Approach to Identify Protective Effects of Palonosetron in Cisplatin-Induced Nephrotoxicity." Pharmaceuticals 13, no. 12 (2020): 480. http://dx.doi.org/10.3390/ph13120480.

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Cisplatin is widely used to treat various types of cancers, but it is often limited by nephrotoxicity. Here, we employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN). Using publicly available mouse kidney and human kidney organoid transcriptome datasets, we first identified a 208-gene expression signature for CIN and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN. We also searched the adverse event database, Food and Drug Administration. Adverse Event Reporting System (FAERS), to identify drugs that reduce the reporting odds ratio of developing cisplatin-induced acute kidney injury. Palonosetron, a serotonin type 3 receptor (5-hydroxytryptamine receptor 3 (5-HT3R)) antagonist, was identified by both CLUE and FAERS analyses. Notably, clinical data from 103 patients treated with cisplatin for head and neck cancer revealed that palonosetron was superior to ramosetron in suppressing cisplatin-induced increases in serum creatinine and blood urea nitrogen levels. Moreover, palonosetron significantly increased the survival rate of zebrafish exposed to cisplatin but not to other 5-HT3R antagonists. These results not only suggest that palonosetron can suppress CIN but also support the use of in silico and in vivo approaches in drug repositioning studies.

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5

Brazas, M. D., J. T. Yamada, and B. F. F. Ouellette. "Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory." Nucleic Acids Research 37, Web Server (2009): W3—W5. http://dx.doi.org/10.1093/nar/gkp531.

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6

Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.

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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .

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Brazas, M. D., J. T. Yamada, and B. F. F. Ouellette. "Providing web servers and training in Bioinformatics: 2010 update on the Bioinformatics Links Directory." Nucleic Acids Research 38, Web Server (2010): W3—W6. http://dx.doi.org/10.1093/nar/gkq553.

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8

Gierut, Aleksandra M., Pawel Dabrowski-Tumanski, Wanda Niemyska, Kenneth C. Millett, and Joanna I. Sulkowska. "PyLink: a PyMOL plugin to identify links." Bioinformatics 35, no. 17 (2019): 3166–68. http://dx.doi.org/10.1093/bioinformatics/bty1038.

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Abstract Summary Links are generalization of knots, that consist of several components. They appear in proteins, peptides and other biopolymers with disulfide bonds or ions interactions giving rise to the exceptional stability. Moreover because of this stability such biopolymers are the target of commercial and medical use (including anti-bacterial and insecticidal activity). Therefore, topological characterization of such biopolymers, not only provides explanation of their thermodynamical or mechanical properties, but paves the way to design templates in pharmaceutical applications. However, distinction between links and trivial topology is not an easy task. Here, we present PyLink—a PyMOL plugin suited to identify three types of links and perform comprehensive topological analysis of proteins rich in disulfide or ion bonds. PyLink can scan for the links automatically, or the user may specify their own components, including closed loops with several bridges and ion interactions. This creates the possibility of designing new biopolymers with desired properties. Availability and implementation The PyLink plugin, manual and tutorial videos are available at http://pylink.cent.uw.edu.pl.

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9

Jabbari, Kosar, Garrett Winkelmaier, Cody Andersen, et al. "Protein Ligands in the Secretome of CD36+ Fibroblasts Induce Growth Suppression in a Subset of Breast Cancer Cell Lines." Cancers 13, no. 18 (2021): 4521. http://dx.doi.org/10.3390/cancers13184521.

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Reprogramming the tumor stroma is an emerging approach to circumventing the challenges of conventional cancer therapies. This strategy, however, is hampered by the lack of a specific molecular target. We previously reported that stromal fibroblasts (FBs) with high expression of CD36 could be utilized for this purpose. These studies are now expanded to identify the secreted factors responsible for tumor suppression. Methodologies included 3D colonies, fluorescent microscopy coupled with quantitative techniques, proteomics profiling, and bioinformatics analysis. The results indicated that the conditioned medium (CM) of the CD36+ FBs caused growth suppression via apoptosis in the triple-negative cell lines of MDA-MB-231, BT549, and Hs578T, but not in the ERBB2+ SKBR3. Following the proteomics and bioinformatic analysis of the CM of CD36+ versus CD36− FBs, we determined KLF10 as one of the transcription factors responsible for growth suppression. We also identified FBLN1, SLIT3, and PENK as active ligands, where their minimum effective concentrations were determined. Finally, in MDA-MB-231, we showed that a mixture of FBLN1, SLIT3, and PENK could induce an amount of growth suppression similar to the CM of CD36+ FBs. In conclusion, our findings suggest that these ligands, secreted by CD36+ FBs, can be targeted for breast cancer treatment.

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10

Hadavi, Razie, Samira Mohammadi-Yeganeh, Javad Razaviyan, Ameneh Koochaki, Parviz Kokhaei, and Ahmadreza Bandegi. "Expression of Bioinformatically Candidate miRNAs including, miR-576-5p, miR-501-3p and miR-3143, Targeting PI3K Pathway in Triple-Negative Breast Cancer." Galen Medical Journal 8 (November 10, 2019): 1646. http://dx.doi.org/10.31661/gmj.v8i0.1646.

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Background: Triple-negative breast cancer (TNBC) is an invasive and lethal form of breast cancer. PI3K pathway, which often activated in TNBC patients, can be a target of miRNAs. The purpose of this study was bioinformatic prediction of miRNAs targeting the key genes of this pathway and evaluation of the expression of them and their targets in TNBC. Materials and Methods: We predicted miRNAs targeting PIK3CA and AKT1 genes using bioinformatics tools. Extraction of total RNA, synthesis of cDNA and quantitative real-time polymerase chain reaction were performed from 18 TNBC samples and normal adjacent tissues and cell lines. Results: Our results demonstrated that miR-576-5p, miR-501-3p and miR-3143 were predicted to target PIK3CA, AKT1 and both of these mRNAs, respectively and were down-regulated while their target mRNAs were up-regulated in clinical samples and cell lines. The analysis of the receiver operating characteristic curve was done for the evaluation of the diagnostic value of predicted miRNAs in TNBC patients. Conclusion: The findings of our study demonstrated the reverse correlation between miRNAs and their target genes and therefore the possibility of these miRNAs to be proposed as new candidates for TNBC targeted therapies. [GMJ.2019;8:e1646]

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11

Brazas, M. D., D. S. Yim, J. T. Yamada, and B. F. F. Ouellette. "The 2011 bioinformatics links directory update: more resources, tools and databases and features to empower the bioinformatics community." Nucleic Acids Research 39, suppl (2011): W3—W7. http://dx.doi.org/10.1093/nar/gkr514.

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12

Naderi-Meshkin, Hojjat, Xin Lai, Raheleh Amirkhah, Julio Vera, John E. J. Rasko, and Ulf Schmitz. "Exosomal lncRNAs and cancer: connecting the missing links." Bioinformatics 35, no. 2 (2018): 352–60. http://dx.doi.org/10.1093/bioinformatics/bty527.

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13

Via, A., A. Zanzoni, and M. Helmer-Citterich. "Seq2Struct: a resource for establishing sequence-structure links." Bioinformatics 21, no. 4 (2004): 551–53. http://dx.doi.org/10.1093/bioinformatics/bti049.

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14

Wu, J., S. Kasif, and C. DeLisi. "Identification of functional links between genes using phylogenetic profiles." Bioinformatics 19, no. 12 (2003): 1524–30. http://dx.doi.org/10.1093/bioinformatics/btg187.

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15

Achard, F., C. Cussat-Blanc, E. Viara, and E. Barillot. "The new Virgil database: a service of rich links." Bioinformatics 14, no. 4 (1998): 342–48. http://dx.doi.org/10.1093/bioinformatics/14.4.342.

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16

Covell, David G. "Bioinformatic analysis linking genomic defects to chemosensitivity and mechanism of action." PLOS ONE 16, no. 4 (2021): e0243336. http://dx.doi.org/10.1371/journal.pone.0243336.

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A joint analysis of the NCI60 small molecule screening data, their genetically defective genes, and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-Organizing-Maps (SOMs) are used to organize the chemosensitivity data. Student’s t-tests are used to identify SOM clusters with enhanced chemosensitivity for tumor cell lines with versus without genetically defective genes. Fisher’s exact and chi-square tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. These results find exploitable instances of enhanced chemosensitivity of compound MOA’s for selected defective genes. Collectively these findings will advance the interpretation of pre-clinical screening data as well as contribute towards the goals of cancer drug discovery, development decision making, and explanation of drug mechanisms.

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17

Obaid, Halah, Sunand Kannappan, Mehul Gupta, et al. "In Vitro Investigation Demonstrates IGFR/VEGFR Receptor Cross Talk and Potential of Combined Inhibition in Pediatric Central Nervous System Atypical Teratoid Rhabdoid Tumors." Current Cancer Drug Targets 20, no. 4 (2020): 295–305. http://dx.doi.org/10.2174/1568009619666191111153049.

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Background: Atypical teratoid rhabdoid tumor of the central nervous system (CNS ATRT) is a malignancy that commonly affects young children. The biological mechanisms contributing to tumor aggressiveness and resistance to conventional therapies in ATRT are unknown. Previous studies have shown the activity of insulin like growth factor-I receptor (IGF-1R) in ATRT tumor specimens and cell lines. IGF-1R has been shown to cross-talk with other receptor tyrosine kinases (RTKs) in a number of cancer types, leading to enhanced cell proliferation. Objective: This study aims to evaluate the role of IGF-1 receptor cross-talk in ATRT biology and the potential for therapeutic targeting. Methods: Cell lines derived from CNS ATRT specimens were analyzed for IGF-1 mediated cell proliferation. A comprehensive receptor tyrosine kinase (RTK) screen was conducted following IGF-1 stimulation. Bioinformatic analysis of publicly available cancer growth inhibition data to identify correlation between IC50 of a VEGFR inhibitor and IGF-1R expression. Results: Comprehensive RTK screen identified VEGFR-2 cross-activation following IGF-1 stimulation. Bioinformatics analysis demonstrated a positive correlation between IC50 values of VEGFR inhibitor Axitinib and IGF-1R expression, supporting the critical influence of IGF-1R in modulating response to anti-angiogenic therapies. Conclusion: Overall, our data present a novel experimental framework to evaluate and utilize receptor cross-talk mechanisms to select effective drugs and combinations for future therapeutic trials in ATRT.

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18

Fox, J. A., S. L. Butland, S. McMillan, G. Campbell, and B. F. F. Ouellette. "The Bioinformatics Links Directory: a Compilation of Molecular Biology Web Servers." Nucleic Acids Research 33, Web Server (2005): W3—W24. http://dx.doi.org/10.1093/nar/gki594.

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19

Srinivasan, P., and B. Libbus. "Mining MEDLINE for implicit links between dietary substances and diseases." Bioinformatics 20, Suppl 1 (2004): i290—i296. http://dx.doi.org/10.1093/bioinformatics/bth914.

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20

Aoki, K. F., H. Mamitsuka, T. Akutsu, and M. Kanehisa. "A score matrix to reveal the hidden links in glycans." Bioinformatics 21, no. 8 (2004): 1457–63. http://dx.doi.org/10.1093/bioinformatics/bti193.

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21

Achard, F., and P. Dessen. "GenXref. VI: Automatic generation of links between two heterogeneous databases." Bioinformatics 14, no. 1 (1998): 20–24. http://dx.doi.org/10.1093/bioinformatics/14.1.20.

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22

Zhang, Zi-Chao, Xiao-Fei Zhang, Min Wu, Le Ou-Yang, Xing-Ming Zhao, and Xiao-Li Li. "A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks." Bioinformatics 36, no. 11 (2020): 3474–81. http://dx.doi.org/10.1093/bioinformatics/btaa157.

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Abstract Motivation Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information. Results In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks. Availability and implementation The package is available at https://github.com/happyalfred2016/GRGMF. Contact leouyang@szu.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.

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Tummanatsakun, Doungdean, Tanakorn Proungvitaya, Sittiruk Roytrakul, and Siriporn Proungvitaya. "Bioinformatic Prediction of Signaling Pathways for Apurinic/Apyrimidinic Endodeoxyribonuclease 1 (APEX1) and Its Role in Cholangiocarcinoma Cells." Molecules 26, no. 9 (2021): 2587. http://dx.doi.org/10.3390/molecules26092587.

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Apurinic/apyrimidinic endodeoxyribonuclease 1 (APEX1) is involved in the DNA damage repair pathways and associates with the metastasis of several human cancers. However, the signaling pathway of APEX1 in cholangiocarcinoma (CCA) has never been reported. In this study, to predict the signaling pathways of APEX1 and related proteins and their functions, the effects of APEX1 gene silencing on APEX1 and related protein expression in CCA cell lines were investigated using mass spectrometry and bioinformatics tools. Bioinformatic analyses predicted that APEX1 might interact with cell division cycle 42 (CDC42) and son of sevenless homolog 1 (SOS1), which are involved in tumor metastasis. RNA and protein expression levels of APEX1 and its related proteins, retrieved from the Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas databases, revealed that their expressions were higher in CCA than in the normal group. Moreover, higher levels of APEX1 expression and its related proteins were correlated with shorter survival time. In conclusion, the signaling pathway of APEX1 in metastasis might be mediated via CDC42 and SOS1. Furthermore, expression of APEX1 and related proteins is able to predict poor survival of CCA patients.

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24

Nehm, Ross H., and Ann F. Budd. "Missing “Links” in Bioinformatics Education: Expanding Students' Conceptions of Bioinformatics Using a Biodiversity Database of Living & Fossil Reef Corals." American Biology Teacher 68, no. 7 (2006): e91-e97. http://dx.doi.org/10.1662/0002-7685(2006)68[91:mlibee]2.0.co;2.

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25

Oshita, Kazuki, Masaru Tomita, and Kazuharu Arakawa. "G-Links: a gene-centric link acquisition service." F1000Research 3 (November 19, 2014): 285. http://dx.doi.org/10.12688/f1000research.5754.1.

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With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/

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Oshita, Kazuki, Masaru Tomita, and Kazuharu Arakawa. "G-Links: a gene-centric link acquisition service." F1000Research 3 (November 18, 2015): 285. http://dx.doi.org/10.12688/f1000research.5754.2.

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With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to provide an identifier conversion and data aggregation system as a part of solution to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/

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27

Lustgarten, J. L., C. Kimmel, H. Ryberg, and W. Hogan. "EPO-KB: a searchable knowledge base of biomarker to protein links." Bioinformatics 24, no. 11 (2008): 1418–19. http://dx.doi.org/10.1093/bioinformatics/btn125.

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Lin, Yang, Xiaoyong Pan, and Hong-Bin Shen. "lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning." Bioinformatics 37, no. 16 (2021): 2308–16. http://dx.doi.org/10.1093/bioinformatics/btab127.

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Abstract Motivation Long non-coding RNAs (lncRNAs) are generally expressed in a tissue-specific way, and subcellular localizations of lncRNAs depend on the tissues or cell lines that they are expressed. Previous computational methods for predicting subcellular localizations of lncRNAs do not take this characteristic into account, they train a unified machine learning model for pooled lncRNAs from all available cell lines. It is of importance to develop a cell-line-specific computational method to predict lncRNA locations in different cell lines. Results In this study, we present an updated cell-line-specific predictor lncLocator 2.0, which trains an end-to-end deep model per cell line, for predicting lncRNA subcellular localization from sequences. We first construct benchmark datasets of lncRNA subcellular localizations for 15 cell lines. Then we learn word embeddings using natural language models, and these learned embeddings are fed into convolutional neural network, long short-term memory and multilayer perceptron to classify subcellular localizations. lncLocator 2.0 achieves varying effectiveness for different cell lines and demonstrates the necessity of training cell-line-specific models. Furthermore, we adopt Integrated Gradients to explain the proposed model in lncLocator 2.0, and find some potential patterns that determine the subcellular localizations of lncRNAs, suggesting that the subcellular localization of lncRNAs is linked to some specific nucleotides. Availabilityand implementation The lncLocator 2.0 is available at www.csbio.sjtu.edu.cn/bioinf/lncLocator2 and the source code can be found at https://github.com/Yang-J-LIN/lncLocator2.

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Stoll, Gautier, Jacques Rougemont, and Félix Naef. "Few crucial links assure checkpoint efficiency in the yeast cell-cycle network." Bioinformatics 22, no. 20 (2006): 2539–46. http://dx.doi.org/10.1093/bioinformatics/btl432.

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Wang, Yan, Miguel Correa Marrero, Marnix H. Medema, and Aalt D. J. van Dijk. "Coevolution-based prediction of protein–protein interactions in polyketide biosynthetic assembly lines." Bioinformatics 36, no. 19 (2020): 4846–53. http://dx.doi.org/10.1093/bioinformatics/btaa595.

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Abstract Motivation Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein–protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. Availability and implementation The code is available on http://www.bif.wur.nl/ (under ‘Software’). Supplementary information Supplementary data are available at Bioinformatics online.

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Cavalcante, Raymond G., Snehal Patil, Terry E. Weymouth, Kestutis G. Bendinskas, Alla Karnovsky, and Maureen A. Sartor. "ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts." Bioinformatics 32, no. 10 (2016): 1536–43. http://dx.doi.org/10.1093/bioinformatics/btw016.

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Cuff, Justin, Keyan Salari, Nicole Clarke, et al. "Integrative Bioinformatics Links HNF1B with Clear Cell Carcinoma and Tumor-Associated Thrombosis." PLoS ONE 8, no. 9 (2013): e74562. http://dx.doi.org/10.1371/journal.pone.0074562.

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Fox, J. A., S. McMillan, and B. F. F. Ouellette. "Conducting Research on the Web: 2007 Update for the Bioinformatics Links Directory." Nucleic Acids Research 35, Web Server (2007): W3—W5. http://dx.doi.org/10.1093/nar/gkm459.

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Brazas, M. D., J. A. Fox, T. Brown, S. McMillan, and B. F. F. Ouellette. "Keeping pace with the data: 2008 update on the Bioinformatics Links Directory." Nucleic Acids Research 36, Web Server (2008): W2—W4. http://dx.doi.org/10.1093/nar/gkn399.

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Iwata, Michio, Longhao Yuan, Qibin Zhao, et al. "Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm." Bioinformatics 35, no. 14 (2019): i191—i199. http://dx.doi.org/10.1093/bioinformatics/btz313.

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Abstract Motivation Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications. Results Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches. Supplementary information Supplementary data are available at Bioinformatics online.

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Basu, Amrita, Ritwik Mitra, Han Liu, Stuart L. Schreiber, and Paul A. Clemons. "RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines." Bioinformatics 34, no. 19 (2018): 3332–39. http://dx.doi.org/10.1093/bioinformatics/bty199.

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Zhang, Ying, Jianliang Xu, Shaoquan Zhang, et al. "HOXA-AS2 Promotes Proliferation and Induces Epithelial-Mesenchymal Transition via the miR-520c-3p/GPC3 Axis in Hepatocellular Carcinoma." Cellular Physiology and Biochemistry 50, no. 6 (2018): 2124–38. http://dx.doi.org/10.1159/000495056.

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Background/Aims: Previous studies have demonstrated that long non-coding RNAs (lncRNAs) may play critical roles in cancer biology, including Hepatocellular carcinoma (HCC). The HOXA cluster antisense RNA2 (HOXA-AS2) lncRNA plays an important role in carcinogenesis, however, the underlying role of HOXA-AS2 in HCC remains unknown. The present study examined the effects of HOXA-AS2 on the progression of HCC, and explored the underlying molecular mechanisms. Methods: Quantitative real-time PCR was used to detect HOXA-AS2 expression in HCC tissues and cell lines. Furthermore, the effects of HOXA-AS2 silencing and overexpression on cell proliferation, cell cycle, apoptosis, migration, and invasion were assessed in HCC in vitro and in vivo. Furthermore, bioinformatics online programs predicted and luciferase reporter assay were used to validate the association of HOXA-AS2 and miR-520c-3p in HCC cells. Results: We observed that HOXA-AS2 was up-regulated in HCC tissues and cell lines. In vitro experiments revealed that HOXA-AS2 knockdown significantly inhibited HCC cells proliferation by causing G1 arrest and promoting apoptosis, whereas HOXA-AS2 overexpression promoted cell growth. Further functional assays indicated that HOXA-AS2 significantly promoted HCC cell migration and invasion by promoting EMT. Bioinformatics online programs predicted that HOXA-AS2 sponge miR-520c-3p at 3’-UTR with complementary binding sites, which was validated using luciferase reporter assay. HOXA-AS2 could negatively regulate the expression of miR-520c-3p in HCC cells. MiR-520c-3p was down-regulated and inversely correlated with HOXA-AS2 expression in HCC tissues. miR-520c-3p suppressed cell proliferation, invasion and migration in HCC cells, and enforced expression of miR-520c-3p attenuated the oncogenic effects of HOXA-AS2 in HCC cells. By bioinformatic analysis and dual-luciferase reporter assay, we found that miR-223-3p directly targeted the 3’-untranslated region (UTR) of Glypican-3 (GPC3), one of the key players in HCC. GPC3 was up-regulated in HCC tissues, and was negatively correlated with miR-520c-3p expression and positively correlated with HOXA-AS2 expression. Conclusion: In summary, our results suggested that the HOXA-AS2/miR-520c-3p/GPC3 axis may play an important role in the regulation of PTC progression, which could serve as a biomarker and therapeutic target for HCC.

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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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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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Boyd, Joseph C., Alice Pinheiro, Elaine Del Nery, Fabien Reyal, and Thomas Walter. "Domain-invariant features for mechanism of action prediction in a multi-cell-line drug screen." Bioinformatics 36, no. 5 (2019): 1607–13. http://dx.doi.org/10.1093/bioinformatics/btz774.

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Abstract Motivation High-content screening is an important tool in drug discovery and characterization. Often, high-content drug screens are performed on one single-cell line. Yet, a single-cell line cannot be thought of as a perfect disease model. Many diseases feature an important molecular heterogeneity. Consequently, a drug may be effective against one molecular subtype of a disease, but less so against another. To characterize drugs with respect to their effect not only on one cell line but on a panel of cell lines is therefore a promising strategy to streamline the drug discovery process. Results The contribution of this article is 2-fold. First, we investigate whether we can predict drug mechanism of action (MOA) at the molecular level without optimization of the MOA classes to the screen specificities. To this end, we benchmark a set of algorithms within a conventional pipeline, and evaluate their MOA prediction performance according to a statistically rigorous framework. Second, we extend this conventional pipeline to the simultaneous analysis of multiple cell lines, each manifesting potentially different morphological baselines. For this, we propose multi-task autoencoders, including a domain-adaptive model used to construct domain-invariant feature representations across cell lines. We apply these methods to a pilot screen of two triple negative breast cancer cell lines as models for two different molecular subtypes of the disease. Availability and implementation https://github.com/jcboyd/multi-cell-line or https://zenodo.org/record/2677923. Supplementary information Supplementary data are available at Bioinformatics online.

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Falque, M. "IRILmap: linkage map distance correction for intermated recombinant inbred lines/advanced recombinant inbred strains." Bioinformatics 21, no. 16 (2005): 3441–42. http://dx.doi.org/10.1093/bioinformatics/bti543.

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Lee, C. y., and L. Chen. "Alternative polyadenylation sites reveal distinct chromatin accessibility and histone modification in human cell lines." Bioinformatics 29, no. 14 (2013): 1713–17. http://dx.doi.org/10.1093/bioinformatics/btt288.

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Huang, B. Emma, and Andrew W. George. "R/mpMap: a computational platform for the genetic analysis of multiparent recombinant inbred lines." Bioinformatics 27, no. 5 (2011): 727–29. http://dx.doi.org/10.1093/bioinformatics/btq719.

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Zhuang, Hongqin, Ziyi Gan, Weiwei Jiang, Xiangyu Zhang, and Zi-Chun Hua. "Functional specific roles of FADD: comparative proteomic analyses from knockout cell lines." Molecular BioSystems 9, no. 8 (2013): 2063–78. http://dx.doi.org/10.1039/c3mb70023b.

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Yang, Jianghong, Ao Li, Yongqiang Li, Xiangqian Guo, and Minghui Wang. "A novel approach for drug response prediction in cancer cell lines via network representation learning." Bioinformatics 35, no. 9 (2018): 1527–35. http://dx.doi.org/10.1093/bioinformatics/bty848.

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Nguyen, N., H. Huang, S. Oraintara, and A. Vo. "Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet." Bioinformatics 26, no. 18 (2010): i659—i665. http://dx.doi.org/10.1093/bioinformatics/btq397.

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Luna, Augustin, Vinodh N. Rajapakse, Fabricio G. Sousa, et al. "rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R." Bioinformatics 32, no. 8 (2015): 1272–74. http://dx.doi.org/10.1093/bioinformatics/btv701.

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Chi, Calvin, Yuting Ye, Bin Chen, and Haiyan Huang. "Bipartite graph-based approach for clustering of cell lines by gene expression–drug response associations." Bioinformatics 37, no. 17 (2021): 2617–26. http://dx.doi.org/10.1093/bioinformatics/btab143.

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Abstract Motivation In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene–drug association patterns and biological context may not be obvious. Results We present a procedure to compare cell lines based on their gene–drug association patterns. Starting with a grouping of cell lines from biological annotation, we model gene–drug association patterns for each group as a bipartite graph between genes and drugs. This is accomplished by applying sparse canonical correlation analysis (SCCA) to extract the gene–drug associations, and using the canonical vectors to construct the edge weights. Then, we introduce a nuclear norm-based dissimilarity measure to compare the bipartite graphs. Accompanying our procedure is a permutation test to evaluate the significance of similarity of cell line groups in terms of gene–drug associations. In the pharmacogenomic datasets CTRP2, GDSC2 and CCLE, hierarchical clustering of carcinoma groups based on this dissimilarity measure uniquely reveals clustering patterns driven by carcinoma subtype rather than primary site. Next, we show that the top associated drugs or genes from SCCA can be used to characterize the clustering patterns of haematopoietic and lymphoid malignancies. Finally, we confirm by simulation that when drug responses are linearly dependent on expression, our approach is the only one that can effectively infer the true hierarchy compared to existing approaches. Availability and implementation Bipartite graph-based hierarchical clustering is implemented in R and can be obtained from CRAN: https://CRAN.R-project.org/package=hierBipartite. The source code is available at https://github.com/CalvinTChi/hierBipartite. The datasets were derived from sources in the public domain, which are the Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ccle), the Cancer Therapeutics Response Portal (https://portals.broadinstitute.org/ctrp.v2.1/?page=#ctd2BodyHome), and the Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/). These datasets can be downloaded using the PharmacoGx R package (https://bioconductor.org/packages/release/bioc/html/PharmacoGx.html). Supplementary information Supplementary data are available at Bioinformatics online.

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Brazas, M. D., D. Yim, W. Yeung, and B. F. F. Ouellette. "A decade of web server updates at the bioinformatics links directory: 2003-2012." Nucleic Acids Research 40, W1 (2012): W3—W12. http://dx.doi.org/10.1093/nar/gks632.

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Chen, Jia-Bin, Shan-Shan Dong, Shi Yao, et al. "Modeling circRNA expression pattern with integrated sequence and epigenetic features demonstrates the potential involvement of H3K79me2 in circRNA expression." Bioinformatics 36, no. 18 (2020): 4739–48. http://dx.doi.org/10.1093/bioinformatics/btaa567.

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Abstract Motivation CircRNAs are an abundant class of non-coding RNAs with widespread, cell-/tissue-specific patterns. Previous work suggested that epigenetic features might be related to circRNA expression. However, the contribution of epigenetic changes to circRNA expression has not been investigated systematically. Here, we built a machine learning framework named CIRCScan, to predict circRNA expression in various cell lines based on the sequence and epigenetic features. Results The predicted accuracy of the expression status models was high with area under the curve of receiver operating characteristic (ROC) values of 0.89–0.92 and the false-positive rates of 0.17–0.25. Predicted expressed circRNAs were further validated by RNA-seq data. The performance of expression-level prediction models was also good with normalized root-mean-square errors of 0.28–0.30 and Pearson’s correlation coefficient r over 0.4 in all cell lines, along with Spearman's correlation coefficient ρ of 0.33–0.46. Noteworthy, H3K79me2 was highly ranked in modeling both circRNA expression status and levels across different cells. Further analysis in additional nine cell lines demonstrated a significant enrichment of H3K79me2 in circRNA flanking intron regions, supporting the potential involvement of H3K79me2 in circRNA expression regulation. Availability and implementation The CIRCScan assembler is freely available online for academic use at https://github.com/johnlcd/CIRCScan. Supplementary information Supplementary data are available at Bioinformatics online.

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