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Relevant bibliographies by topics / LINCS bioinformatics

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Academic literature on the topic 'LINCS bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "LINCS bioinformatics"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "LINCS bioinformatics"

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Cai, Xiaoshu. "DEVELOPMENT OF COMPUTATIONAL APPROACH FOR DRUG DISCOVERY." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1465403528.

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Sullivan, Courtney R. "Bioenergetic Abnormalities in Schizophrenia." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1523629996205968.

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Mahaye, Ntombikayise. "A central enrichment-based comparison of two alternative methods of generating transcription factor binding motifs from protein binding microarray data." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1003049.

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Characterising transcription factor binding sites (TFBS) is an important problem in bioinformatics, since predicting binding sites has many applications such as predicting gene regulation. ChIP-seq is a powerful in vivo method for generating genome-wide putative binding regions for transcription factors (TFs). CentriMo is an algorithm that measures central enrichment of a motif and has previously been used as motif enrichment analysis (MEA) tool. CentriMo uses the fact that ChIP-seq peak calling methods are likely to be biased towards the centre of the putative binding region, at least in cases where there is direct binding. CentriMo calculates a binomial p-value representing central enrichment, based on the central bias of the binding site with the highest likelihood ratio. In cases where binding is indirect or involves cofactors, a more complex distribution of preferred binding sites may occur but, in many cases, a low CentriMo p-value and low width of maximum enrichment (about 100bp) are strong evidence that the motif in question is the true binding motif. Several other MEA tools have been developed, but they do not consider motif central enrichment. The study investigates the claim made by Zhao and Stormo (2011) that they have identified a simpler method than that used to derive the UniPROBE motif database for creating motifs from protein binding microarray (PBM) data, which they call BEEML-PBM (Binding Energy Estimation by Maximum Likelihood-PBM). To accomplish this, CentriMo is employed on 13 motifs from both motif databases. The results indicate that there is no conclusive difference in the quality of motifs from the original PBM and BEEML-PBM approaches. CentriMo provides an understanding of the mechanisms by which TFs bind to DNA. Out of 13 TFs for which ChIP-seq data is used, BEEML-PBM reports five better motifs and twice it has not had any central enrichment when the best PBM motif does. PBM approach finds seven motifs with better central enrichment. On the other hand, across all variations, the number of examples where PBM is better is not high enough to conclude that it is overall the better approach. Some TFs bind directly to DNA, some indirect or in combination with other TFs. Some of the predicted mechanisms are supported by literature evidence. This study further revealed that the binding specificity of a TF is different in different cell types and development stages. A TF is up-regulated in a cell line where it performs its biological function. The discovery of cell line differences, which has not been done before in any CentriMo study, is interesting and provides reasons to study this further.

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Migeon, Pierre. "Comparative genomics of repetitive elements between maize inbred lines B73 and Mo17." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35377.

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Master of Science<br>Genetics Interdepartmental Program<br>Sanzhen Liu<br>The major component of complex genomes is repetitive elements, which remain recalcitrant to characterization. Using maize as a model system, we analyzed whole genome shotgun (WGS) sequences for the two maize inbred lines B73 and Mo17 using k-mer analysis to quantify the differences between the two genomes. Significant differences were identified in highly repetitive sequences, including centromere, 45S ribosomal DNA (rDNA), knob, and telomere repeats. Genotype specific 45S rDNA sequences were discovered. The B73 and Mo17 polymorphic k-mers were used to examine allele-specific expression of 45S rDNA in the hybrids. Although Mo17 contains higher copy number than B73, equivalent levels of overall 45S rDNA expression indicates that transcriptional or post-transcriptional regulation mechanisms operate for the 45S rDNA in the hybrids. Using WGS sequences of B73xMo17 doubled haploids, genomic locations showing differential repetitive contents were genetically mapped, revealing differences in organization of highly repetitive sequences between the two genomes. In an analysis of WGS sequences of HapMap2 lines, including maize wild progenitor, landraces, and improved lines, decreases and increases in abundance of additional sets of k-mers associated with centromere, 45S rDNA, knob, and retrotransposons were found among groups, revealing global evolutionary trends of genomic repeats during maize domestication and improvement.

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Bagshaw, Andrew Tobias Matthew. "An Investigation of Links Between Simple Sequences and Meiotic Recombination Hotspots." Thesis, University of Canterbury. Biological Sciences, 2008. http://hdl.handle.net/10092/1597.

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Previous evidence has shown that the simple sequences microsatellites and poly-purine/poly-pyrimidine tracts (PPTs) could be both a cause, and an effect, of meiotic recombination. The causal link between simple sequences and recombination has not been much explored, however, probably because other evidence has cast doubt on its generality, though this evidence has never been conclusive. Several questions have remained unanswered in the literature, and I have addressed aspects of three of them in my thesis. First, what is the scale and magnitude of the association between simple sequences and recombination? I found that microsatellites and PPTs are strongly associated with meiotic double-strand break (DSB) hotspots in yeast, and that PPTs are generally more common in human recombination hotspots, particularly in close proximity to hotspot central regions, in which recombination events are markedly more frequent. I also showed that these associations can't be explained by coincidental mutual associations between simple sequences, recombination and other factors previously shown to correlate with both. A second question not conclusively answered in the literature is whether simple sequences, or their high levels of polymorphism, are an effect of recombination. I used three methods to address this question. Firstly, I investigated the distributions of two-copy tandem repeats and short PPTs in relation to yeast DSB hotspots in order to look for evidence of an involvement of recombination in simple sequence formation. I found no significant associations. Secondly, I compared the fraction of simple sequences containing polymorphic sites between human recombination hotspots and coldspots. The third method I used was generalized linear model analysis, with which I investigated the correlation between simple sequence variation and recombination rate, and the influence on the correlation of additional factors with potential relevance including GC-content and gene density. Both the direct comparison and correlation methods showed a very weak and inconsistent effect of recombination on simple sequence polymorphism in the human genome.Whether simple sequences are an important cause of recombination events is a third question that has received relatively little previous attention, and I have explored one aspect of it. Simple sequences of the types I studied have previously been shown to form non-B-DNA structures, which can be recombinagenic in model systems. Using a previously described sodium bisulphite modification assay, I tested for the presence of these structures in sequences amplified from the central regions of hotspots and cloned into supercoiled plasmids. I found significantly higher sensitivity to sodium bisulphite in humans in than in chimpanzees in three out of six genomic regions in which there is a hotspot in humans but none in chimpanzees. In the DNA2 hotspot, this correlated with a clear difference in numbers of molecules showing long contiguous strings of converted cytosines, which are present in previously described intramolecular quadruplex and triplex structures. Two out of the five other hotspots tested show evidence for secondary structure comparable to a known intramolecular triplex, though with similar patterns in humans and chimpanzees. In conclusion, my results clearly motivate further investigation of a functional link between simple sequences and meiotic recombination, including the putative role of non-B-DNA structures.

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Stanfield, Zachary. "Comprehensive Characterization of the Transcriptional Signaling of Human Parturition through Integrative Analysis of Myometrial Tissues and Cell Lines." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1562863761406809.

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Denecker, Thomas. "Bioinformatique et analyse de données multiomiques : principes et applications chez les levures pathogènes Candida glabrata et Candida albicans Functional networks of co-expressed genes to explore iron homeostasis processes in the pathogenic yeast Candida glabrata Efficient, quick and easy-to-use DNA replication timing analysis with START-R suite FAIR_Bioinfo: a turnkey training course and protocol for reproducible computational biology Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility Rendre ses projets R plus accessibles grâce à Shiny Pixel: a content management platform for quantitative omics data Empowering the detection of ChIP-seq "basic peaks" (bPeaks) in small eukaryotic genomes with a web user-interactive interface A hypothesis-driven approach identifies CDK4 and CDK6 inhibitors as candidate drugs for treatments of adrenocortical carcinomas Characterization of the replication timing program of 6 human model cell lines." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL010.

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Plusieurs évolutions sont constatées dans la recherche en biologie. Tout d’abord, les études menées reposent souvent sur des approches expérimentales quantitatives. L’analyse et l’interprétation des résultats requièrent l’utilisation de l’informatique et des statistiques. Également, en complément des études centrées sur des objets biologiques isolés, les technologies expérimentales haut débit permettent l’étude des systèmes (caractérisation des composants du système ainsi que des interactions entre ces composants). De très grandes quantités de données sont disponibles dans les bases de données publiques, librement réutilisables pour de nouvelles problématiques. Enfin, les données utiles pour les recherches en biologie sont très hétérogènes (données numériques, de textes, images, séquences biologiques, etc.) et conservées sur des supports d’information également très hétérogènes (papiers ou numériques). Ainsi « l’analyse de données » s’est petit à petit imposée comme une problématique de recherche à part entière et en seulement une dizaine d’années, le domaine de la « Bioinformatique » s’est en conséquence totalement réinventé. Disposer d’une grande quantité de données pour répondre à un questionnement biologique n’est souvent pas le défi principal. La vraie difficulté est la capacité des chercheurs à convertir les données en information, puis en connaissance. Dans ce contexte, plusieurs problématiques de recherche en biologie ont été abordées lors de cette thèse. La première concerne l’étude de l’homéostasie du fer chez la levure pathogène Candida glabrata. La seconde concerne l’étude systématique des modifications post-traductionnelles des protéines chez la levure pathogène Candida albicans. Pour ces deux projets, des données « omiques » ont été exploitées : transcriptomiques et protéomiques. Des outils bioinformatiques et des outils d’analyses ont été implémentés en parallèle conduisant à l’émergence de nouvelles hypothèses de recherche en biologie. Une attention particulière et constante a aussi été portée sur les problématiques de reproductibilité et de partage des résultats avec la communauté scientifique<br>Biological research is changing. First, studies are often based on quantitative experimental approaches. The analysis and the interpretation of the obtained results thus need computer science and statistics. Also, together with studies focused on isolated biological objects, high throughput experimental technologies allow to capture the functioning of biological systems (identification of components as well as the interactions between them). Very large amounts of data are also available in public databases, freely reusable to solve new open questions. Finally, the data in biological research are heterogeneous (digital data, texts, images, biological sequences, etc.) and stored on multiple supports (paper or digital). Thus, "data analysis" has gradually emerged as a key research issue, and in only ten years, the field of "Bioinformatics" has been significantly changed. Having a large amount of data to answer a biological question is often not the main challenge. The real challenge is the ability of researchers to convert the data into information and then into knowledge. In this context, several biological research projects were addressed in this thesis. The first concerns the study of iron homeostasis in the pathogenic yeast Candida glabrata. The second concerns the systematic investigation of post-translational modifications of proteins in the pathogenic yeast Candida albicans. In these two projects, omics data were used: transcriptomics and proteomics. Appropriate bioinformatics and analysis tools were developed, leading to the emergence of new research hypotheses. Particular and constant attention has also been paid to the question of data reproducibility and sharing of results with the scientific community

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Phatak, Amruta Rajendra. "Modeling cancer predisposition: Profiling Li-Fraumeni syndrome patient-derived cell lines using bioinformatics and three-dimensional culture models." 2015. http://hdl.handle.net/1805/8037.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Although rare, classification of over 200 hereditary cancer susceptibility syndromes accounting for ~5-10% of cancer incidence has enabled the discovery and understanding of cancer predisposition genes that are also frequently mutated in sporadic cancers. The need to prevent or delay invasive cancer can partly be addressed by characterization of cells derived from healthy individuals predisposed to cancer due to inherited "single-hits" in genes in order to develop patient-derived samples as preclinical models for mechanistic in vitro studies. Here, we present microarray-based transcriptome profiling of Li-Fraumeni syndrome (LFS) patient-derived unaffected breast epithelial cells and their phenotypic characterization as in vitro three-dimensional (3D) models to test pharmacological agents. In this study, the epithelial cells derived from the unaffected breast tissue of a LFS patient were cultured and progressed from non-neoplastic to a malignant stage by successive immortalization and transformation steps followed by growth in athymic mice. These cell lines exhibited distinct transcriptomic profiles and were readily distinguishable based upon their gene expression patterns, growth characteristics in monolayer and in vitro 3D cultures. Transcriptional changes in the epithelial-to-mesenchymal transition gene signature contributed to the unique phenotypes observed in 3D culture for each cell line of the progression series; the fully transformed LFS cells exhibited invasive processes in 3D culture with disorganized morphologies due to cell-cell miscommunication, as seen in breast cancer. Bioinformatics analysis of the deregulated genes and pathways showed inherent differences between these cell lines and targets for pharmacological agents. After treatment with small molecule APR-246 that restores normal function to mutant p53, we observed that the neoplastic LFS cells had reduced malignant invasive structure formation from 73% to 9%, as well as an observance of an increase in formation of well-organized structures in 3D culture (from 27% to 91%) by stereomicroscopy and confocal microscopy. Therefore, the use of well-characterized and physiologically relevant preclinical models in conjunction with transcriptomic profiling of high-risk patient derived samples as a renewable laboratory resource can potentially guide the development of safer and more effective chemopreventive approaches.

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El-Hachem, Nehme. "Analyse transcriptomique et applications en développement préclinique des médicaments." Thèse, 2016. http://hdl.handle.net/1866/18556.

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L’émergence des Mégadonnées (« Big Data ») en biologie moléculaire, surtout à travers la transcriptomique, a révolutionné la façon dont nous étudions diverses disciplines telles que le processus de développement du médicament ou la recherche sur le cancer. Ceci fut associé à un nouveau concept, la médecine de précision, dont le principal but est de comprendre les mécanismes moléculaires entraînant une meilleure réponse thérapeutique chez le patient. Cette thèse est à mi-chemin entre les études pharmaco — et toxicogénomiques expérimentales, et les études cliniques et translationnelles. Le but de cette thèse est surtout de montrer le potentiel et les limites de ces jeux de données et leur pertinence pour la découverte de biomarqueurs de réponse ainsi que la compréhension des mécanismes d’action/toxicité de médicaments, en vue d’utiliser ces informations à des fins thérapeutiques. L’originalité de cette thèse réside dans son approche globale pour analyser les plus larges jeux de données pharmaco/toxicogénomiques publiés à ce jour et ceci pour : 1) Aborder la notion de biomarqueurs de réponse aux médicaments en pharmacogénomique du cancer, en étudiant les facteurs discordants entre deux grandes études publiées en 2012; 2) Comprendre le mécanisme d’action des médicaments et construire une taxonomie performante en utilisant une approche intégrative; et 3) Créer un répertoire toxicogénomique à partir des hépatocytes humains, exposés à différentes classes de médicaments et composés chimiques. Mes contributions principales sont les suivantes : • J’ai développé une approche bioinformatique pour étudier les facteurs discordants entre deux grandes études pharmacogénomiques et suggérées que les différences observées émergeaient plutôt de l’absence de standardisation des mesures pharmacologiques qui pourrait limiter la validation de biomarqueurs de réponse aux médicaments. • J’ai implémenté une approche bioinformatique qui montre la supériorité de l’intégration tenant en compte des différents paramètres pour les médicaments (structure, cytotoxicité, perturbation du transcriptome) afin d’élucider leur mécanisme d’action (MoA). • J’ai développé un pipeline bioinformatique pour étudier le niveau de conservation des mécanismes moléculaires entre les études toxicogénomiques in vivo et in vitro démontrant que les hépatocytes humains sont un modèle fiable pour détecter les produits toxiques hépatocarcinogènes. Au total, nos études ont permis de fournir un cadre de travail original pour l’exploitation de différents types de données transcriptomiques pour comprendre l’impact des produits chimiques sur la biologie cellulaire.<br>The emergence of Big Data in molecular biology, especially through the study of transcriptomics, has revolutionized the way we look at various disciplines, such as drug development and cancer research. Big data analysis is an important part of the concept of precision medicine, which primary purpose is to understand the molecular mechanisms leading to better therapeutic response in patients. This thesis is halfway between pharmaco-toxicogenomics experimental studies, and clinical and translational studies. The aim of this thesis is mainly to show the potential and limitations of these studies and their relevance, especially for the discovery of drug response biomarkers and understanding the drug mechanisms (targets, toxicities). This thesis is an original work since it proposes a global approach to analyzing the largest pharmaco-toxicogenomic datasets available to date. The key aims were: 1) Addressing the challenge of reproducibility for biomarker discovery in cancer pharmacogenomics, by comparing two large pharmacogenomics studies published in 2012; 2) Understanding drugs mechanism of action using an integrative approach to generate a superior drug-taxonomy; and 3) Evaluating the conservation of toxicogenomic responses in primary hepatocytes vs. in vivo liver samples in order to check the feasability of cell models in toxicology studies. My main contributions can be summarized as follow: - I developed a bioinformatics pipeline to study the factors that trigger (in)consistency between two major pharmacogenomic studies. I suggested that the observed differences emerged from the non-standardization of pharmacological measurements, which could limit the validation of drug response biomarker. - I implemented a bioinformatics pipeline that demonstrated the superiority of the integrative approach, since it takes into account different parameters for the drug (structure, cytotoxicity, transcriptional perturbation) to elucidate the mechanism of action (MoA). - I developed a bioinformatics pipeline to study the level of conservation of toxicity mechanisms between the in vivo and in vitro system, showing that human hepatocytes is a reliable model for hepatocarcinogens testing. Overall, our studies have provided a unique framework to leverage various types of transcriptomic data in order to understand the impact of chemicals on cell biology.

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(10725786), James Michael Amstutz. "Cluster-Based Analysis Of Retinitis Pigmentosa Candidate Modifiers Using Drosophila Eye Size And Gene Expression Data." Thesis, 2021.

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Abstract:

<p>The goal of this thesis is to algorithmically identify candidate modifiers for <i>retinitis pigmentosa</i> (RP) to help improve therapy and predictions for this genetic disorder that may lead to a complete loss of vision. A current research by (Chow et al., 2016) focused on the genetic contributors to RP by trying to recognize a correlation between genetic modifiers and phenotypic variation in female <i>Drosophila melanogaster</i>, or fruit flies. In comparison to the genome-wide association analysis carried out in Chow et al.’s research, this study proposes using a K-Means clustering algorithm on RNA expression data to better understand which genes best exhibit characteristics of the RP degenerative model. Validating this algorithm’s effectiveness in identifying suspected genes takes priority over their classification.</p><p>This study investigates the linear relationship between <i>Drosophila </i>eye size and genetic expression to gather statistically significant, strongly correlated genes from the clusters with abnormally high or low eye sizes. The clustering algorithm is implemented in the R scripting language, and supplemental information details the steps of this computational process. Running the mean eye size and genetic expression data of 18,140 female <i>Drosophila</i> genes and 171 strains through the proposed algorithm in its four variations helped identify 140 suspected candidate modifiers for retinal degeneration. Although none of the top candidate genes found in this study matched Chow’s candidates, they were all statistically significant and strongly correlated, with several showing links to RP. These results may continue to improve as more of the 140 suspected genes are annotated using identical or comparative approaches.</p>

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Book chapters on the topic "LINCS bioinformatics"

1

Lu, Xiyuan, Cuihong Dai, Aiju Hou, Jie Cui, Dayou Cheng, and Dechang Xu. "Dysregulated microRNA Profile in HeLa Cell Lines Induced by Lupeol." In Bioinformatics Research and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08171-7_7.

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Knight, V. Bleu, and Elba E. Serrano. "RNA Sequencing Analysis of Neural Cell Lines: Impact of Normalization and Technical Replication." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56154-7_41.

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Tomaszek, Sandra, and Dennis S. Tomaszek. "Cell Lines, Tissue Samples, Model Organisms, and Biobanks: Infrastructure and Tools for Cancer Systems Biology." In Cancer Systems Biology, Bioinformatics and Medicine. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1567-7_4.

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Mueller, Nikola S., Ivan Kondofersky, Gökcen Eraslan, Karolina Worf, and Fabian J. Theis. "Bioinformatics in Psychiatric Genetics." In Psychiatric Genetics. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190221973.003.0009.

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Abstract:

Bioinformatics in psychiatric research is surveyed by exploring data analysis techniques tailored to extract information from genetic studies. Genome-wide association studies (GWAS) play a major role by providing links between certain psychiatric diseases and single-nucleotide polymorphisms (SNPs). A challenge in identifying such links is prioritizing SNPs, and we give an overview of state-of-the-art prioritization methods and discuss current trends. Furthermore, we give details on post-GWAS analysis by introducing network-based knowledge. The aim of incorporating network information either in the sense of local proximity or similar function, is the formulation of multivariate models where multiple SNPs may have synergistic effects on a disease that cannot be inferred univariately. Finally, we provide a list of a combination of available tools and biological databases of identified genetic associations to psychiatric diseases. These open up the possibility for researchers to use published results and thus profit from findings in their respective fields.

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Conference papers on the topic "LINCS bioinformatics"

1

Isik, Riza, Isiksu Eksioglu, Bahattin Can Maral, Benan Bardak, and Mehmet Tan. "Chemical Induced Differential Gene Expression Prediction on LINCS Database." In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2020. http://dx.doi.org/10.1109/bibe50027.2020.00026.

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Zhang, Xu, Karen E. Ross, Tapan K. Maity, Jake Jaffe, Cathy H. Wu, and Udayan Guha. "PTM Knowledge Networks and LINCS Multi-Omics Data for Kinase Inhibitor Drug-Analytics in Lung Cancer." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, 2018. http://dx.doi.org/10.1145/3233547.3233633.

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Pawełkowicz, Magdalena E., Michał Wojcieszek, Paweł Osipowski, Tomasz Krzywkowski, Wojciech Pląder, and Zbigniew Przybecki. "Identification and bioinformatics comparison of two novel phosphatases in monoecious and gynoecious cucumber lines." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, edited by Ryszard S. Romaniuk. SPIE, 2016. http://dx.doi.org/10.1117/12.2249061.

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"Photosynthesis of the isogenic lines Triticum aestivum L." In SYSTEMS BIOLOGY AND BIOINFORMATICS (SBB-2020). Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences., 2020. http://dx.doi.org/10.18699/sbb-2020-34.

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"Alloplasmic lines (Hordeum vulgare)-Triticum aestivum with complete cytonuclear compatibility are the sources of introgression DH lines for wheat breeding." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-154.

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"Alloplasmic wheat lines, their photosynthetic activity and drought-tolerance." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-192.

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7

"Barley alloplasmic lines – the spectra of peculiar plasmon types." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-175.

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"The genetic variability of proliferative cell lines of Larix sibirica." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-197.

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Moldavanov, A. V. "Analytical and Numerical Model for Evolution of Minimal Cell with Infinite Number of Energy Links." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.15.

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"Triticale-wheat hybrid lines with the vaviloid type of spike branching." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-004.

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