Protein Kinase MpYAK1 Is Involved in Meristematic Cell Proliferation, Reproductive Phase Change and Nutrient Signaling in the Liverwort Marchantia polymorpha

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Abstract

Plant growth and development are regulated by environmental factors, including nutrient availability and light conditions, via endogenous genetic signaling pathways. Phosphorylation-dependent protein modification plays a major role in the regulation of cell proliferation in stress conditions, and several protein kinases have been shown to function in response to nutritional status, including dual-specificity tyrosine phosphorylation-regulated kinases (DYRKs). Although DYRKs are widely conserved in eukaryotes, the physiological functions of DYRKs in land plants are still to be elucidated. In the liverwort Marchantia polymorpha, a model bryophyte, four putative genes encoding DYRK homologous proteins, each of which belongs to the subfamily yet another kinase 1 (Yak1), plant-specific DYRK, DYRK2, or pre-mRNA processing protein 4 kinase, were identified. MpYAK1-defective male and female mutant lines generated by the clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated nuclease 9 (Cas9) system showed smaller sizes of thalli than did the wild-type plants and repressed cell divisions in the apical notch regions. The Mpyak1 mutants developed rhizoids from gemmae in the gemma cup before release. The Mpyak1 lines developed sexual organs even in non-inductive short-day photoperiod conditions supplemented with far-red light. In nitrogen (N)-deficient conditions, rhizoid elongation was inhibited in the Mpyak1 mutants. In conditions of aeration with 0.08% CO2 (v/v) and N depletion, Mpyak1 mutants accumulated higher levels of sucrose and lower levels of starch compared to the wild type. Transcriptomic analyses revealed that the expression of peroxidase genes was differentially affected by MpYAK1. These results suggest that MpYAK1 is involved in the maintenance of plant growth and developmental responses to light conditions and nutrient signaling.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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HTSeq—a Python framework to work with high-throughput sequencing data

Simon Anders, Paul Pyl, Wolfgang Huber (2014)

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update

Tian Tian, Yue Qian Liu, Hengyu Yan … (2017)

Abstract The agriGO platform, which has been serving the scientific community for >10 years, specifically focuses on gene ontology (GO) enrichment analyses of plant and agricultural species. We continuously maintain and update the databases and accommodate the various requests of our global users. Here, we present our updated agriGO that has a largely expanded number of supporting species (394) and datatypes (865). In addition, a larger number of species have been classified into groups covering crops, vegetables, fish, birds and insects closely related to the agricultural community. We further improved the computational efficiency, including the batch analysis and P-value distribution (PVD), and the user-friendliness of the web pages. More visualization features were added to the platform, including SEACOMPARE (cross comparison of singular enrichment analysis), direct acyclic graph (DAG) and Scatter Plots, which can be merged by choosing any significant GO term. The updated platform agriGO v2.0 is now publicly accessible at http://systemsbiology.cau.edu.cn/agriGOv2/.