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Molecular Research of Multi-omics in Cancer
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Molecular Research of Multi-omics in Cancer

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3982

Special Issue Editor

Dr. Siddharth Pratap

E-Mail Website
Guest Editor
Department of Microbiology, Immunology, and Physiology: Bioinformatics Core, Meharry Medical College, Nashville, TN 37208, USA
Interests: bioinformatics; genomics; transcriptomics; proteomics; biomedical informatics; health disparities

Special Issue Information

Dear Colleagues,

Bioinformatics and “omics”-level technologies have revolutionized cancer research by utilizing computational methods to analyze large-scale biological data. High-throughput sequencing (HTS), such as DNA-Seq and RNA-Seq, combined with advances in proteomics-scale mass spectrometry have significantly contributed to molecular biology methods in discovering novel pathways in cancer. Advanced algorithms have facilitated the discovery of biomarkers for early detection and personalized treatment strategies. Bioinformatics contributes to precision medicine, allowing researchers to uncover molecular intricacies and develop targeted therapies, significantly advancing our understanding and treatment of cancer.

This Special Issue aims to contribute to the evolving landscape of cancer research by leveraging advanced bioinformatics and molecular biology techniques. By focusing on bioinformatics utilizing high-throughput sequencing techniques (DNA-Seq, RNA-Seq, ChIP-Seq) and/or proteomics, we hope to uncover novel perspectives that will advance our understanding of cancer and its potential implications in research and treatment. Researchers are encouraged to submit review and original research articles that contribute to the collective knowledge in this critical and rapidly evolving field.

Dr. Siddharth Pratap
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioinformatics
  • biomedical informatics
  • high-throughput sequencing (HTS)/next-generation sequencing (NGS)
  • DNA-Seq/genomics
  • RNA-Req/transcriptomics
  • mass spectrometry/proteomics

 

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Published Papers (2 papers)

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Research

22 pages, 3439 KiB  
Article
A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer
by Rossano Atzeni, Matteo Massidda, Enrico Pieroni, Vincenzo Rallo, Massimo Pisu and Andrea Angius
Int. J. Mol. Sci. 2024, 25(15), 8044; https://doi.org/10.3390/ijms25158044 - 24 Jul 2024
Viewed by 1331
Abstract
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues [...] Read more.
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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21 pages, 9299 KiB  
Article
The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome
by Victor Paromov, Vladimir N. Uversky, Ayorinde Cooley, Lincoln E. Liburd II, Shyamali Mukherjee, Insung Na, Guy W. Dayhoff II and Siddharth Pratap
Int. J. Mol. Sci. 2024, 25(3), 1552; https://doi.org/10.3390/ijms25031552 - 26 Jan 2024
Viewed by 2226
Abstract
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human [...] Read more.
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed “the unfoldome”. Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network. Full article
(This article belongs to the Special Issue Molecular Research of Multi-omics in Cancer)
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