MIDGET:Detecting differential gene expression on microarray data

Angelescu, Radu; Dobrescu, Radu

doi:10.1016/j.cmpb.2021.106418

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…SRA contains massive amount of raw RNA-sequencing (RNAseq) data of various experiments from multiple biological material and under multiple conditions, truly unpolished diamonds for researchers. Nevertheless, the common clinician and researcher do not possess the skills to preprocess such data—the exact same situation regarding WES and WGS data that GeniePool solves by preprocessing the raw data beforehand and making it accessible with now available algorithms that can deal with extended amount of RNAseq samples ( 23 ). The same basic idea behind GeniePool can serve as the basis for making accessible the continuously uploaded raw RNAseq data uploaded onto SRA, with data lakes being the preferred way to efficiently confront the challenge of managing SRA’s constantly growing data loads.…”

Section: Discussionmentioning

confidence: 99%

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

et al. 2023

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In recent years, there are a huge influx of genomic data and a growing need for its phenotypic correlations, yet existing genomic databases do not allow easy storage and accessibility to the combined phenotypic–genotypic information. Freely accessible allele frequency (AF) databases, such as gnomAD, are crucial for evaluating variants but lack correlated phenotype data. The Sequence Read Archive (SRA) accumulates hundreds of thousands of next-generation sequencing (NGS) samples tagged by their submitters and various attributes. However, samples are stored in large raw format files, inaccessible for a common user. To make thousands of NGS samples and their corresponding additional attributes easily available to clinicians and researchers, we generated a pipeline that continuously downloads raw human NGS data uploaded to SRA using SRAtoolkit and preprocesses them using GATK pipeline. Data are then stored efficiently in a cloud data lake and can be accessed via a representational state transfer application programming interface (REST API) and a user-friendly website. We thus generated GeniePool, a simple and intuitive web service and API for querying NGS data from SRA with direct access to information related to each sample and related studies, providing significant advantages over existing databases for both clinical and research usages. Utilizing data lake infrastructure, we were able to generate a multi-purpose tool that can serve many clinical and research use cases. We expect users to explore the meta-data served via GeniePool both in daily clinical practice and in versatile research endeavours. Database URL https://geniepool.link

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Section: Discussionmentioning

confidence: 99%

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

et al. 2023

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“…Recently, high-throughput sequencing and the gene chip technology have been widely used in the field of life science ( 14 , 15 ). Bioinformatics is an important tool for analyzing large volumes of existing biological data.…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis

Yang

Lu²,

Peng³

et al. 2022

Front. Immunol.

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BackgroundColon cancer (CC) is the second most common gastrointestinal malignancy. About one in five patients have already developed distant metastases at the time of initial diagnosis, and up to half of patients develop distant metastases from initial local disease, which leads to a poor prognosis for CC patients. Necroptosis plays a key role in promoting tumor growth in different tumors. The purpose of this study was to construct a prognostic model composed of necroptosis-related genes (NRGs) in CC.MethodsThe Cancer Genome Atlas was used to obtain information on clinical features and gene expression. Gene expression differential analysis, weighted gene co-expression network analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator regression algorithm were utilized to identify prognostic NRGs. Thereafter, a risk scoring model was established based on the NRGs. Biological processes and pathways were identified by gene ontology and gene set enrichment analysis (GSEA). Further, protein-protein interaction and ceRNA networks were constructed based on mRNA-miRNA-lncRNA. Finally, the effect of necroptosis related risk score on different degrees of immune cell infiltration was evaluated.ResultsCALB1, CHST13, and SLC4A4 were identified as NRGs of prognostic significance and were used to establish a risk scoring model. The time-dependent receiver operating characteristic curve analysis revealed that the model could well predict the 1-, 3-, and 5-year overall survival (OS). Further, GSEA suggested that the NRGs may participate in biological processes, such as the WNT pathway and JAK-Stat pathway. Eight key hub genes were identified, and a ceRNA regulatory network, which comprised 1 lncRNA, 5 miRNAs and 3 mRNAs, was constructed. Immune infiltration analysis revealed that the low-risk group had significantly higher immune-related scores than the high-risk group. A nomogram of the model was constructed based on the risk score, necroptosis, and the clinicopathological features (age and TNM stage). The calibration curves implied that the model was effective at predicting the 1-, 3-, and 5-year OS of CC.ConclusionOur NRG-based prognostic model can assist in the evaluation of CC prognosis and the identification of therapeutic targets for CC.

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MIDGET:Detecting differential gene expression on microarray data

Cited by 2 publications

References 18 publications

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

Integrated analysis of necroptosis-related genes for evaluating immune infiltration and colon cancer prognosis

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