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      A modERN resource: identification of Drosophila transcription factor candidate target genes using RNAi

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          Abstract

          Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change −3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies ( ERR, sens, and zfh2) and two previously uncharacterized TFs ( sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC ( http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              Proteomics. Tissue-based map of the human proteome.

              Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Genetics
                Genetics
                genetics
                Genetics
                Oxford University Press (US )
                0016-6731
                1943-2631
                April 2023
                19 January 2023
                19 January 2023
                : 223
                : 4
                : iyad004
                Affiliations
                Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA
                Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA
                Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA
                Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA
                Department of Genome Sciences, University of Washington School of Medicine , Seattle, WA 98195, USA
                Department of Genome Sciences, University of Washington School of Medicine , Seattle, WA 98195, USA
                Department of Genome Sciences, University of Washington School of Medicine , Seattle, WA 98195, USA
                Department of Genome Sciences, University of Washington School of Medicine , Seattle, WA 98195, USA
                Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory , Berkeley, CA 94720, USA
                Author notes
                Corresponding author: Division of Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, 1 Cyclotron Rd, MS 977, Berkeley, CA 94720, USA. Email: secelniker@ 123456lbl.gov

                William W Fisher, Ann S Hammonds and Richard Weiszmann contributed equally to this work.

                Conflicts of interest None declared.

                Article
                iyad004
                10.1093/genetics/iyad004
                10078917
                36652461
                6307e806-400a-41de-b865-77171a7cbaf3
                © The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 November 2022
                : 22 December 2022
                : 15 February 2023
                Page count
                Pages: 16
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                drosophila,transcription factors,regulation,embryo and gene expression
                Genetics
                drosophila, transcription factors, regulation, embryo and gene expression

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