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Transcriptome analyses using high-throughput methodologies allow a deeper understanding of biological functions in different cell types/tissues. The present study provides an RNA-seq profiling of human sperm mRNAs and lncRNAs (messenger and long non-coding RNAs) in a well-characterized population of fertile individuals. Sperm RNA was extracted from twelve ejaculate samples under strict quality controls. Poly(A)-transcripts were sequenced and aligned to the human genome. mRNAs and lncRNAs were classified according to their mean expression values (FPKM: Fragments Per Kilobase of transcript per Million mapped reads) and integrity. Gene Ontology analysis of the Expressed and Highly Expressed mRNAs showed an involvement in diverse reproduction processes, while the Ubiquitously Expressed and Highly Stable mRNAs were mainly involved in spermatogenesis. Transcription factor enrichment analyses revealed that the Highly Expressed and Ubiquitously Expressed sperm mRNAs were primarily regulated by zinc-fingers and spermatogenesis-related proteins. Regarding the Expressed lncRNAs, only one-third of their potential targets corresponded to Expressed mRNAs and were enriched in cell-cycle regulation processes. The remaining two-thirds were absent in sperm and were enriched in embryogenesis-related processes. A significant amount of post-testicular sperm mRNAs and lncRNAs was also detected. Even though our study is solely directed to the poly-A fraction of sperm transcripts, results indicate that both sperm mRNAs and lncRNAs constitute a footprint of previous spermatogenesis events and are configured to affect the first stages of embryo development., (© The Author(s) 2021. Published by Oxford University Press on behalf of European Society of Human Reproduction and Embryology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The acute phase protein orosomucoid-1 (Orm1) is mainly expressed by hepatocytes (HPCs) under stress conditions. However, its specific function is not fully understood. Here, we report a role of Orm1 as an executer of HPC proliferation. Increases in serum levels of Orm1 were observed in patients after surgical resection for liver cancer and in mice undergone partial hepatectomy (PH). Transcriptome study showed that Orm1 became the most abundant in HPCs isolated from regenerating mouse liver tissues after PH. Both in vitro and in vivo siRNA-induced knockdown of Orm1 suppressed proliferation of mouse regenerating HPCs and human hepatic cells. Microarray analysis in regenerating mouse livers revealed that the signaling pathways controlling chromatin replication, especially the minichromosome maintenance protein complex genes were uniformly down-regulated following Orm1 knockdown. These data suggest that Orm1 is induced in response to hepatic injury and executes liver regeneration by activating cell cycle progression in HPCs., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Cap analysis of gene expression (CAGE) is a sequencing based technology to capture the 5' ends of RNAs in a biological sample. After mapping, a CAGE peak on the genome indicates the position of an active transcriptional start site (TSS) and the number of reads correspond to its expression level. CAGE is prominently used in both the FANTOM and ENCODE project but presently there is no software package to perform the essential data processing steps., Results: Here we describe MOIRAI, a compact yet flexible workflow system designed to carry out the main steps in data processing and analysis of CAGE data. MOIRAI has a graphical interface allowing wet-lab researchers to create, modify and run analysis workflows. Embedded within the workflows are graphical quality control indicators allowing users assess data quality and to quickly spot potential problems. We will describe three main workflows allowing users to map, annotate and perform an expression analysis over multiple samples., Conclusions: Due to the many built in quality control features MOIRAI is especially suitable to support the development of new sequencing based protocols., Availiability: The MOIRAI source code is freely available at http://sourceforge.net/projects/moirai/.

Motivation: Clustering co-expressed genes usually requires the definition of 'distance' or 'similarity' between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data., Results: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.