MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts

The study of tissue organization and morphogenesis requires quantitative analysis of 3D biological samples, a challenging task owing to limitations in imaging dense organs at single-cell resolution. Current 3D segmentation and quantification tools often struggle with the low resolution and signal-to-noise ratios typical of images taken in vivo or deep within tissues. To address this, we developed Object Stitching by Clustering of Adjacent Regions (OSCAR), a framework that combines machine learning with non-linear fitting and statistical algorithms specifically designed to quantify biological 3D stacks with high cellular density and low signal-to-background ratio based on nuclear staining. As proof of principle, the framework is applied to quantify the growth and organizational dynamics of the developing zebrafish vertebrate retina, showing that the cell numbers increase exponentially, while cell density increases and average nuclear volume decreases. Overall, its high accuracy, ease of use and reduced computational requirements establish OSCAR as a valuable tool for automated image analysis of densely packed tissues composed of cell subtypes that can be distinguished by specific labeling.

BackgroundAutotetraploid Carassius auratus (4n = 200, RRRR) (abbreviated as 4nRR) is derived from whole genome duplication of Carassius auratus red var. (2n = 100, RR) (abbreviated as RCC). Ribosome DNA (rDNA) is often used to study molecular evolution of repeated sequences because it has high copy number and special conserved coding regions in genomes. In this study, we analysed the sequences (5S, ITS1-5.8S-ITS2 region), structure, methylation level (NTS and IGS), and expression level (5S and 18S) of 5S and 45S ribosomal RNA (rRNA) genes in 4nRR and RCC in order to elucidate the effects of autotetraploidization on rDNA in fish.ResultsResults showed that there was high sequence similarity of 5S, 5.8S and ITS1 region between 4nRR and RCC. This study also identified two different types of ITS2 region in 4nRR and predicted the secondary structure of ITS2. It turns out that both secondary structures are functional. Compared with RCC, there was no significant difference in NTS (5S rRNA) methylation level, but the expression level of 5S rRNA was lower in 4nRR, indicating that methylation had little effect on the expression level in 4nRR. IGS (45S rRNA) was hypermethylated in 4nRR compared to RCC, but the expression of 18S rRNA gene was no significantly different from that in RCC, indicating that methylation regulation affected gene expression in 4nRR.ConclusionThe above studies initially revealed the effects of autotetraploidization on the structure and function of 5S and 45S rRNA in Carassius auratus, and provided a theoretical support for the systematic study of the evolution pattern and characteristics of rDNA in vertebrates.

In silico imaging clinical trials are emerging alternative sources of evidence for regulatory evaluation and are typically cheaper and faster than human trials. In this Note, we describe the set of in silico imaging software tools used in the VICTRE (Virtual Clinical Trial for Regulatory Evaluation) which replicated a traditional trial using a computational pipeline. We describe a complete imaging clinical trial software package for comparing two breast imaging modalities (digital mammography and digital breast tomosynthesis). First, digital breast models were developed based on procedural generation techniques for normal anatomy. Second, lesions were inserted in a subset of breast models. The breasts were imaged using GPU-accelerated Monte Carlo transport methods and read using image interpretation models for the presence of lesions. All in silico components were assembled into a computational pipeline. The VICTRE images were made available in DICOM format for ease of use and visualization. We describe an open-source collection of in silico tools for running imaging clinical trials. All tools and source codes have been made freely available. The open-source tools distributed as part of the VICTRE project facilitate the design and execution of other in silico imaging clinical trials. The entire pipeline can be run as a complete imaging chain, modified to match needs of other trial designs, or used as independent components to build additional pipelines.

BackgroundThe advent of large-scale gene expression technologies has helped to reveal in eukaryotic cells, the existence of thousands of non-coding transcripts, whose function and significance remain mostly poorly understood. Among these non-coding transcripts, long non-coding RNAs (lncRNAs) are the least well-studied but are emerging as key regulators of diverse cellular processes. In the present study, we performed a survey in bovine Longissimus thoraci of lincRNAs (long intergenic non-coding RNAs not overlapping protein-coding transcripts). To our knowledge, this represents the first such study in bovine muscle.ResultsTo identify lincRNAs, we used paired-end RNA sequencing (RNA-Seq) to explore the transcriptomes of Longissimus thoraci from nine Limousin bull calves. Approximately 14–45 million paired-end reads were obtained per library. A total of 30,548 different transcripts were identified. Using a computational pipeline, we defined a stringent set of 584 different lincRNAs with 418 lincRNAs found in all nine muscle samples. Bovine lincRNAs share characteristics seen in their mammalian counterparts: relatively short transcript and gene lengths, low exon number and significantly lower expression, compared to protein-encoding genes. As for the first time, our study identified lincRNAs from nine different samples from the same tissue, it is possible to analyse the inter-individual variability of the gene expression level of the identified lincRNAs. Interestingly, there was a significant difference when we compared the expression variation of the 418 lincRNAs with the 10,775 known selected protein-encoding genes found in all muscle samples. In addition, we found 2,083 pairs of lincRNA/protein-encoding genes showing a highly significant correlated expression. Fourteen lincRNAs were selected and 13 were validated by RT-PCR. Some of the lincRNAs expressed in muscle are located within quantitative trait loci for meat quality traits.ConclusionsOur study provides a glimpse into the lincRNA content of bovine muscle and will facilitate future experimental studies to unravel the function of these molecules. It may prove useful to elucidate their effect on mechanisms underlying the genetic variability of meat quality traits. This catalog will complement the list of lincRNAs already discovered in cattle and therefore will help to better annotate the bovine genome.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-499) contains supplementary material, which is available to authorized users.

A micro electrical impedance spectroscopy (muEIS) system has been developed and implemented to analyze highly and poorly metastatic head and neck cancer (HNC) cell lines with single-cell resolution. The microsystem has arrays of 16 impedance analysis sites, each of which is capable of capturing a single cell and analyzing its whole-cell electrical impedance spectrum. This muEIS system was used to obtain the electrical impedance spectra of the poorly metastatic HNC cell line 686 LN and the highly metastatic HNC cell line 686 LN-M4e over a frequency range of 40 Hz - 10 MHz. The 686 LN cells had higher impedance phase compared to that of 686 LN-M4e cells at frequencies between 50 kHz and 2 MHz. This result demonstrates that the metastatic state of HNC cells can be distinguished using the developed muEIS system. This system is expected to serve as a powerful tool for future detection and quantification of cancer cells from various tumor stages.

Spatial transcriptomics holds great promise for revolutionizing biology and medicine by providing gene expression profiles with spatial information. Until recently, spatial resolution has been limited, but advances in high-throughput in situ imaging technologies now offer new opportunities by covering thousands of genes at a single-cell or even subcellular resolution, necessitating databases dedicated to comprehensive coverage and analysis with user-friendly intefaces. We introduce the HISSTA database, which facilitates the archival and analysis of in situ transcriptome data at single-cell resolution from various human tissues. We have collected and annotated spatial transcriptome data generated by MERFISH, CosMx SMI, and Xenium techniques, encompassing 112 samples and 28 million cells across 16 tissue types from 63 studies. To decipher spatial contexts, we have implemented advanced tools for cell type annotation, spatial colocalization, spatial cellular communication, and niche analyses. Notably, all datasets and annotations are interactively accessible through Vitessce, allowing users to focus on regions of interest and examine gene expression in detail. HISSTA is a unique database designed to manage the rapidly growing dataset of in situ transcriptomes at single-cell resolution. Given its comprehensive data content and advanced analysis tools with interactive visualizations, HISSTA is poised to significantly impact cancer diagnosis, precision medicine, and digital pathology. HISSTA is freely accessible at https://kbds.re.kr/hissta/. The source code is available at https://doi.org/10.5281/zenodo.14904523.

Objective The analysis of mitochondrial DNA has been a potent tool in our understanding of evolution, owing to characteristics such as high copy number, apparent lack of recombination, high substitution rate and maternal mode of inheritance. This study aims to carry outgenetic and phylogenetic analysis on the D-Loop region of the mitochondrial genome in Najdi goats and to evaluate the relations between Najdi goats and other goat breeds available in Iran and around the world. Materials and methods 12 blood samples from unrelated Najdi goats from various areas of Khuzestan Province were collected. After extraction of DNA, a 968 nucleotide fragment of the D-loop region of the mitochondrial genome was amplified and sequenced. Then, the fragments were compared to the other breeds submitted in NCBI database. Finally, a phylogenetic tree was constructed using MEGA6 software. Results The results showed that the Najdi goat breed classified into A Haplogroup and has the lowest genetic distance with the breeds of Chinese and Pakistani goats. The results of genetic variation led to the identification of 20 mutations with 5 Haplotypes in Najdi goat population. Moreover, the haplotype, nucleotide diversities and the average number of pairwise differences were estimated as 0.727, 0.009 and 0.540, respectively; which indicates a high diversity in the Nadji goat. The results obtained from Tajima's D show that in the population of the Najdi goat, a balanced natural selection is going on and the frequency of rare alleles in this population is very low. Conclusions The genetic diversity of Najdi goats has increased over the years, while the number of livestock in Khuzestan province has decreased. The increase in genetic diversity could be due to crossbreed of this breed with Pakistani breeds and so on. The result of these crossbreeding in the near future will be the extinction of Najdi goats, and this issue requires more attention from the authorities.

Decision letter: Epigenetic conservation at gene regulatory elements revealed by non-methylated DNA profiling in seven vertebrates

MotivationAccurate analysis of data-independent acquisition (DIA) mass spectrometry data relies on machine learning to distinguish target peptides from decoy peptides. Different DIA identification engines adopt distinct binary classifiers and training workflows to accomplish this learning task. However, systematic comparisons of how different machine learning strategies affect identification performance are lacking. This absence of evaluation hinders optimal learning strategy selection, increases the risk of model underfitting or overfitting, and ultimately undermines the effectiveness and reliability of false discovery rate (FDR) control.ResultsIn this study, we benchmarked three training strategies and four classifiers on representative DIA datasets. Among them, K-fold training combined with a multilayer perceptron achieved the best balance between identification depth and FDR control. We have released the datasets and code through the Python package Disc-Hub, enabling rapid selection of optimal machine learning configurations for developing DIA identification algorithms.Availability and implementationDisc-Hub is released as an open source software and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/yuyiwen-yiyuwen/Disc_Hub.

BackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs.ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges.ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

Target site analysis of RTE1_LA and its AfroSINE partner in the elephant genome

MetaQuant is a Java-based program for the automatic and accurate quantification of GC/MS-based metabolome data. In contrast to other programs MetaQuant is able to quantify hundreds of substances simultaneously with minimal manual intervention. The integration of a self-acting calibration function allows the parallel and fast calibration for several metabolites simultaneously. Finally, MetaQuant is able to import GC/MS data in the common NetCDF format and to export the results of the quantification into Systems Biology Markup Language (SBML), Comma Separated Values (CSV) or Microsoft Excel (XLS) format. MetaQuant is written in Java and is available under an open source license. Precompiled packages for the installation on Windows or Linux operating systems are freely available for download. The source code as well as the installation packages are available at http://bioinformatics.org/metaquant

MotivationLong-read sequencing technology is becoming an increasingly indispensable tool in genomic and transcriptomic analysis. In transcriptomics in particular, long reads offer the possibility of sequencing full-length isoforms, which can vastly simplify the identification of novel transcripts and transcript quantification. However, despite this promise, the focus of much long-read method development to date has been on transcript identification, with comparatively little attention paid to quantification. Yet, due to differences in the underlying protocols and technologies, lower throughput (i.e. fewer reads sequenced per sample compared to short-read technologies), as well as technical artifacts, long-read quantification remains a challenge, motivating the continued development and assessment of quantification methods tailored to this increasingly prevalent type of data.ResultsWe introduce a new method and corresponding user-friendly software tool for long-read transcript quantification called oarfish. Our model incorporates a novel coverage score, which affects the conditional probability of fragment assignment in the underlying probabilistic model. We demonstrate, in both simulated and experimental data, that by accounting for this coverage information, oarfish is able to produce more accurate quantification estimates than existing long-read quantification tools.Availability and Implementationoarfish is implemented in the Rust programming language and is made available as free and open-source software under the BSD 3-clause license. The source code is available at https://www.github.com/COMBINE-lab/oarfish.

Spatial Analysis and Modeling Tool Version 2 (SAMT2), a spatial modeling tool kit written in Python

Among the most significant findings of the post-genomic era, the discovery of pervasive transcription of mammalian genomes has tremendously modified our understanding of the genome output seen as RNA molecules. The increased focus on non-protein-coding genomic regions together with concomitant technological innovations has led to rapid discovery of numerous noncoding transcripts (ncRNAs). Biological relevance and functional roles of the vast majority of these ncRNAs remain largely unknown.The cap analysis of gene expression (CAGE) technology allows accurate transcript detection and quantification without relying on preexisting transcript models. In combination with complementary data sets, generated using other technologies, it has been shown as an efficient approach for exploring transcriptome complexity.Here, we describe the use of CAGE for the identification of novel noncoding transcripts in mammalian cells providing detailed information for basic data processing and advanced bioinformatics analyses.