scholarly journals Bio301: A Web-Based EST Annotation Pipeline That Facilitates Functional Comparison Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Yen-Chen Chen ◽  
Yun-Ching Chen ◽  
Wen-Dar Lin ◽  
Chung-Der Hsiao ◽  
Hung-Wen Chiu ◽  
...  
Keyword(s):  
Comparative Studies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biological Information ◽  
Microarray Probe ◽  
Annotation Pipeline ◽  
Go Annotation ◽  
Comparison Functions ◽  
Statistical Library ◽  
User Friendly

In this postgenomic era, a huge volume of information derived from expressed sequence tags (ESTs) has been constructed for functional description of gene expression profiles. Comparative studies have become more and more important to researchers of biology. In order to facilitate these comparative studies, we have constructed a user-friendly EST annotation pipeline with comparison tools on an integrated EST service website, Bio301. Bio301 includes regular EST preprocessing, BLAST similarity search, gene ontology (GO) annotation, statistics reporting, a graphical GO browsing interface, and microarray probe selection tools. In addition, Bio301 is equipped with statistical library comparison functions using multiple EST libraries based on GO annotations for mining meaningful biological information.

scholarly journals Robust partial reference-free cell composition estimation from tissue expression

2020 ◽  
Vol 36 (11) ◽  
pp. 3431-3438
Author(s):  
Ziyi Li ◽  
Zhenxing Guo ◽  
Ying Cheng ◽  
Peng Jin ◽  
Hao Wu
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Real Data ◽  
Estimation Procedure ◽  
Free Cell ◽  
Biological Information ◽  
Supplementary Information ◽  
Tissue Samples ◽  
Cell Composition ◽  
Heterogeneous Tissues

Abstract Motivation In the analysis of high-throughput omics data from tissue samples, estimating and accounting for cell composition have been recognized as important steps. High cost, intensive labor requirements and technical limitations hinder the cell composition quantification using cell-sorting or single-cell technologies. Computational methods for cell composition estimation are available, but they are either limited by the availability of a reference panel or suffer from low accuracy. Results We introduce TOols for the Analysis of heterogeneouS Tissues TOAST/-P and TOAST/+P, two partial reference-free algorithms for estimating cell composition of heterogeneous tissues based on their gene expression profiles. TOAST/-P and TOAST/+P incorporate additional biological information, including cell-type-specific markers and prior knowledge of compositions, in the estimation procedure. Extensive simulation studies and real data analyses demonstrate that the proposed methods provide more accurate and robust cell composition estimation than existing methods. Availability and implementation The proposed methods TOAST/-P and TOAST/+P are implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-Wide Analysis of the Role of NAC Family in Flower Development and Abiotic Stress Responses in Cleistogenes songorica

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 927
Author(s):  
Xifang Zong ◽  
Qi Yan ◽  
Fan Wu ◽  
Qian Ma ◽  
Jiyu Zhang
Keyword(s):  
Differential Expression ◽  
Stress Responses ◽  
Expression Profiles ◽  
Crop Improvement ◽  
Gene Expression Profiles ◽  
Purifying Selection ◽  
Go Annotation ◽  
Nac Transcription Factors ◽  
Response To Stress ◽  
Nac Family

Plant-specific NAC (NAM, ATAF, CUC) transcription factor (TF) family plays important roles in biological processes such as plant growth and response to stress. Nevertheless, no information is known about NAC TFs in Cleistogenes songorica, a prominent xerophyte desert grass in northwestern China. In this study, 162 NAC genes were found from the Cleistogenes songorica genome, among which 156 C. songoricaNAC (CsNAC) genes (96.3%) were mapped onto 20 chromosomes. The phylogenetic tree constructed by CsNAC and rice NAC TFs can be separated into 14 subfamilies. Syntenic and Ka/Ks analyses showed that CsNACs were primarily expanded by genomewide replication events, and purifying selection was the primary force driving the evolution of CsNAC family genes. The CsNAC gene expression profiles showed that 36 CsNAC genes showed differential expression between cleistogamous (CL) and chasmogamous (CH) flowers. One hundred and two CsNAC genes showed differential expression under heat, cold, drought, salt and ABA treatment. Twenty-three CsNAC genes were commonly differentially expressed both under stress responses and during dimorphic floret development. Gene Ontology (GO) annotation, coexpression network and qRT-PCR tests revealed that these CsNAC genes may simultaneously regulate dimorphic floret development and the response to stress. Our results may help to characterize the NAC transcription factors in C. songorica and provide new insights into the functional research and application of the NAC family in crop improvement, especially in dimorphic floret plants.

scholarly journals PCaDB - a comprehensive and interactive database for transcriptomes from prostate cancer population cohorts

2021 ◽  
Author(s):  
Ruidong Li ◽  
Zhenyu Jia
Keyword(s):  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Public ◽  
Interactive Analysis ◽  
Single Cell Rna Sequencing ◽  
Transcriptomics Data ◽  
Normal Human ◽  
User Friendly

Prostate cancer (PCa) is a heterogeneous disease with highly variable clinical outcomes which presents enormous challenges in the clinical management. A vast amount of transcriptomics data from large PCa cohorts have been generated, providing extraordinary opportunities for the comprehensive molecular characterization of the PCa disease and development of prognostic signatures to accurately predict the risk of PCa recurrence. The lack of an inclusive collection and standard processing of the public transcriptomics datasets constrains the extensive use of the valuable resources. In this study, we present a user-friendly database, PCaDB, for a comprehensive and interactive analysis and visualization of gene expression profiles from 50 public transcriptomics datasets with 7,231 samples. PCaDB also includes a single-cell RNA-sequencing (scRNAseq) dataset for normal human prostates and 30 published PCa prognostic signatures. The advanced analytical methods equipped in PCaDB would greatly facilitate data mining to understand the heterogeneity of PCa and to develop prognostic signatures and machine learning models for PCa prognosis. PCaDB is publicly available at http://bioinfo.jialab-ucr.org/PCaDB/.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2018 ◽  
Author(s):  
R. Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Tree Structures ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

AbstractAdvances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. Consequently, it becomes possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data and further impute temporal gene expression profiles along the reconstructed tree structures. We demonstrate MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

scholarly journals Diurnal.plant.tools: comparative transcriptomic and co-expression analyses of diurnal gene expression of the Archaeplastida kingdom

2019 ◽  
Author(s):  
Jonathan Wei Xiong Ng ◽  
Qiao Wen Tan ◽  
Camilla Ferrari ◽  
Marek Mutwil
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Species Comparisons ◽  
Gene Modules ◽  
Time Specific ◽  
Almost All ◽  
User Friendly ◽  
Diurnal Regulation

ABSTRACTAlmost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression, and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally-controlled modules across species. The database is freely available at http://diurnal.plant.tools/.

scholarly journals Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2019 ◽  
Vol 47 (17) ◽  
pp. 8961-8974 ◽  
Author(s):  
R Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

Abstract Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

scholarly journals IRIS-DGE: An integrated RNA-seq data analysis and interpretation system for differential gene expression

2018 ◽  
Author(s):  
Brandon Monier ◽  
Adam McDermaid ◽  
Jing Zhao ◽  
Anne Fennell ◽  
Qin Ma
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Rna Seq ◽  
Differential Gene ◽  
User Friendly

AbstractMotivationNext-Generation Sequencing has made available much more large-scale genomic and transcriptomic data. Studies with RNA-sequencing (RNA-seq) data typically involve generation of gene expression profiles that can be further analyzed, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.ResultsHere we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.AvailabilityIRIS-DGE is freely available at http://bmbl.sdstate.edu/IRIS/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Diurnal.plant.tools: Comparative Transcriptomic and Co-expression Analyses of Diurnal Gene Expression of the Archaeplastida Kingdom

2019 ◽  
Vol 61 (1) ◽  
pp. 212-220 ◽  
Author(s):  
Jonathan Wei Xiong Ng ◽  
Qiao Wen Tan ◽  
Camilla Ferrari ◽  
Marek Mutwil
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Species Comparisons ◽  
Gene Modules ◽  
Time Specific ◽  
Almost All ◽  
User Friendly ◽  
Diurnal Regulation

Abstract Almost all organisms coordinate some aspects of their biology through the diurnal cycle. Photosynthetic organisms, and plants especially, have established complex programs that coordinate physiological, metabolic and developmental processes with the changing light. The diurnal regulation of the underlying transcriptional processes is observed when groups of functionally related genes (gene modules) are expressed at a specific time of the day. However, studying the diurnal regulation of these gene modules in the plant kingdom was hampered by the large amount of data required for the analyses. To meet this need, we used gene expression data from 17 diurnal studies spanning the whole Archaeplastida kingdom (Plantae kingdom in the broad sense) to make an online diurnal database. We have equipped the database with tools that allow user-friendly cross-species comparisons of gene expression profiles, entire co-expression networks, co-expressed clusters (involved in specific biological processes), time-specific gene expression and others. We exemplify how these tools can be used by studying three important biological questions: (i) the evolution of cell division, (ii) the diurnal control of gene modules in algae and (iii) the conservation of diurnally controlled modules across species. The database is freely available at http://diurnal.plant.tools.

scholarly journals A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

Blood ◽  
2016 ◽  
Vol 128 (8) ◽  
pp. e20-e31 ◽  
Author(s):  
Sonia Nestorowa ◽  
Fiona K. Hamey ◽  
Blanca Pijuan Sala ◽  
Evangelia Diamanti ◽  
Mairi Shepherd ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stem Cell Differentiation ◽  
Hematopoietic Stem ◽  
Web Resource ◽  
Key Points ◽  
Cell Gene Expression ◽  
User Friendly

Key Points An expression map of HSPC differentiation from single-cell RNA sequencing of HSPCs provides insights into blood stem cell differentiation. A user-friendly Web resource provides access to single-cell gene expression profiles for the wider research community.

scholarly journals Use of relevancy and complementary information for discriminatory gene selection from high-dimensional cancer data

2020 ◽  
Author(s):  
Md Nazmul Haque ◽  
Sadia Sharmin ◽  
Amin Ahsan Ali ◽  
Abu Ashfaqur Sajib ◽  
Mohammad Shoyaib
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Performance ◽  
Global Gene Expression ◽  
Biological Information ◽  
Classification Rate ◽  
Cancer Data

AbstractWith the advent of high-throughput technologies, life sciences are generating a huge amount of biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed or not in a cell or in a tissue at a particular moment under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed in relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly and more significantly attributing to a particular phenotype or condition, such as cancer or disease, de novo. With the increase in the number of genes, simple feature selection methods show poor performance for both selecting the effective and informative features and capturing biological information. Addressing these issues, here we propose Mutual information based Gene Selection method (MGS) for selecting informative genes and two ranking methods based on frequency (MGSf) and Random Forest (MGSrf) for ranking the selected genes. We tested our methods on four real gene expression datasets derived from different studies on cancerous and normal samples. Our methods obtained better classification rate with the datasets compared to recently reported methods. Our methods could also detect the key relevant pathways with a causal relationship to the phenotype.

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