Dataset Information

MicroRNAs: fine tuning of erythropoiesis.

ABSTRACT: Cell proliferation and differentiation is a complex process involving many cellular mechanisms. One of the best-studied phenomena in cell differentiation is erythrocyte development during hematopoiesis in vertebrates. In recent years, a new class of small, endogenous, non-coding RNAs called microRNAs (miRNAs) emerged as important regulators of gene expression at the post-transcriptional level. Thousands of miRNAs have been identified in various organisms, including protozoa, fungi, bacteria and viruses, proving that the regulatory miRNA pathway is conserved in evolution. There are many examples of miRNA-mediated regulation of gene expression in the processes of cell proliferation, differentiation and apoptosis, and in cancer genesis. Many of the collected data clearly show the dependence of the proteome of a cell on the qualitative and quantitative composition of endogenous miRNAs. Numerous specific miRNAs are present in the hematopoietic erythroid line. This review attempts to summarize the state of knowledge on the role of miRNAs in the regulation of different stages of erythropoiesis. Original experimental data and results obtained with bioinformatics tools were combined to elucidate the currently known regulatory network of miRNAs that guide the process of differentiation of red blood cells.

SUBMITTER: Listowski MA

PROVIDER: S-EPMC6276011 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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microRNAs: fine tuning of erythropoiesis.

Listowski Marcin A MA Heger Elżbieta E Bogusławska Dżamila M DM Machnicka Beata B Kuliczkowski Kazimierz K Leluk Jacek J Sikorski Aleksander F AF

Cellular & molecular biology letters 20121103 1

Cell proliferation and differentiation is a complex process involving many cellular mechanisms. One of the best-studied phenomena in cell differentiation is erythrocyte development during hematopoiesis in vertebrates. In recent years, a new class of small, endogenous, non-coding RNAs called microRNAs (miRNAs) emerged as important regulators of gene expression at the post-transcriptional level. Thousands of miRNAs have been identified in various organisms, including protozoa, fungi, bacteria and ...[more]

PMID: 23124859

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Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

MicroRNAs: fine tuning of erythropoiesis.

Publications

microRNAs: fine tuning of erythropoiesis.

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