1 Introduction to Bioinformatics
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T-Coffee Documentation Release Version 13.45.47.Aba98c5
T-Coffee Documentation Release Version_13.45.47.aba98c5 Cedric Notredame Aug 31, 2021 Contents 1 T-Coffee Installation 3 1.1 Installation................................................3 1.1.1 Unix/Linux Binaries......................................4 1.1.2 MacOS Binaries - Updated...................................4 1.1.3 Installation From Source/Binaries downloader (Mac OSX/Linux)...............4 1.2 Template based modes: PSI/TM-Coffee and Expresso.........................5 1.2.1 Why do I need BLAST with T-Coffee?.............................6 1.2.2 Using a BLAST local version on Unix.............................6 1.2.3 Using the EBI BLAST client..................................6 1.2.4 Using the NCBI BLAST client.................................7 1.2.5 Using another client.......................................7 1.3 Troubleshooting.............................................7 1.3.1 Third party packages......................................7 1.3.2 M-Coffee parameters......................................9 1.3.3 Structural modes (using PDB)................................. 10 1.3.4 R-Coffee associated packages................................. 10 2 Quick Start Regressive Algorithm 11 2.1 Introduction............................................... 11 2.2 Installation from source......................................... 12 2.3 Examples................................................. 12 2.3.1 Fast and accurate........................................ 12 2.3.2 Slower and more accurate.................................... 12 2.3.3 Very Fast........................................... -
Whole Genome Sequencing and Comparative Genomic Analysis Of
Li et al. BMC Genomics (2020) 21:181 https://doi.org/10.1186/s12864-020-6593-1 RESEARCH ARTICLE Open Access Whole genome sequencing and comparative genomic analysis of oleaginous red yeast Sporobolomyces pararoseus NGR identifies candidate genes for biotechnological potential and ballistospores-shooting Chun-Ji Li1,2, Die Zhao3, Bing-Xue Li1* , Ning Zhang4, Jian-Yu Yan1 and Hong-Tao Zou1 Abstract Background: Sporobolomyces pararoseus is regarded as an oleaginous red yeast, which synthesizes numerous valuable compounds with wide industrial usages. This species hold biotechnological interests in biodiesel, food and cosmetics industries. Moreover, the ballistospores-shooting promotes the colonizing of S. pararoseus in most terrestrial and marine ecosystems. However, very little is known about the basic genomic features of S. pararoseus. To assess the biotechnological potential and ballistospores-shooting mechanism of S. pararoseus on genome-scale, the whole genome sequencing was performed by next-generation sequencing technology. Results: Here, we used Illumina Hiseq platform to firstly assemble S. pararoseus genome into 20.9 Mb containing 54 scaffolds and 5963 predicted genes with a N50 length of 2,038,020 bp and GC content of 47.59%. Genome completeness (BUSCO alignment: 95.4%) and RNA-seq analysis (expressed genes: 98.68%) indicated the high-quality features of the current genome. Through the annotation information of the genome, we screened many key genes involved in carotenoids, lipids, carbohydrate metabolism and signal transduction pathways. A phylogenetic assessment suggested that the evolutionary trajectory of the order Sporidiobolales species was evolved from genus Sporobolomyces to Rhodotorula through the mediator Rhodosporidiobolus. Compared to the lacking ballistospores Rhodotorula toruloides and Saccharomyces cerevisiae, we found genes enriched for spore germination and sugar metabolism. -
Comparative Analysis of Multiple Sequence Alignment Tools
I.J. Information Technology and Computer Science, 2018, 8, 24-30 Published Online August 2018 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2018.08.04 Comparative Analysis of Multiple Sequence Alignment Tools Eman M. Mohamed Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected]. Hamdy M. Mousa, Arabi E. keshk Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected], [email protected]. Received: 24 April 2018; Accepted: 07 July 2018; Published: 08 August 2018 Abstract—The perfect alignment between three or more global alignment algorithm built-in dynamic sequences of Protein, RNA or DNA is a very difficult programming technique [1]. This algorithm maximizes task in bioinformatics. There are many techniques for the number of amino acid matches and minimizes the alignment multiple sequences. Many techniques number of required gaps to finds globally optimal maximize speed and do not concern with the accuracy of alignment. Local alignments are more useful for aligning the resulting alignment. Likewise, many techniques sub-regions of the sequences, whereas local alignment maximize accuracy and do not concern with the speed. maximizes sub-regions similarity alignment. One of the Reducing memory and execution time requirements and most known of Local alignment is Smith-Waterman increasing the accuracy of multiple sequence alignment algorithm [2]. on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the Table 1. Pairwise vs. multiple sequence alignment most well-known programs (CLUSTAL-OMEGA, PSA MSA MAFFT, BROBCONS, KALIGN, RETALIGN, and Compare two biological Compare more than two MUSCLE). -
How to Generate a Publication-Quality Multiple Sequence Alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012)
Tutorial: How to generate a publication-quality multiple sequence alignment (Thomas Weimbs, University of California Santa Barbara, 11/2012) 1) Get your sequences in FASTA format: • Go to the NCBI website; find your sequences and display them in FASTA format. Each sequence should look like this (http://www.ncbi.nlm.nih.gov/protein/6678177?report=fasta): >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG 2) In a text editor, paste all your sequences together (in the order that you would like them to appear in the end). It should look like this: >gi|6678177|ref|NP_033320.1| syntaxin-4 [Mus musculus] MRDRTHELRQGDNISDDEDEVRVALVVHSGAARLGSPDDEFFQKVQTIRQTMAKLESKVRELEKQQVTIL ATPLPEESMKQGLQNLREEIKQLGREVRAQLKAIEPQKEEADENYNSVNTRMKKTQHGVLSQQFVELINK CNSMQSEYREKNVERIRRQLKITNAGMVSDEELEQMLDSGQSEVFVSNILKDTQVTRQALNEISARHSEI QQLERSIRELHEIFTFLATEVEMQGEMINRIEKNILSSADYVERGQEHVKIALENQKKARKKKVMIAICV SVTVLILAVIIGITITVG >gi|151554658|gb|AAI47965.1| STX3 protein [Bos taurus] MKDRLEQLKAKQLTQDDDTDEVEIAVDNTAFMDEFFSEIEETRVNIDKISEHVEEAKRLYSVILSAPIPE PKTKDDLEQLTTEIKKRANNVRNKLKSMERHIEEDEVQSSADLRIRKSQHSVLSRKFVEVMTKYNEAQVD FRERSKGRIQRQLEITGKKTTDEELEEMLESGNPAIFTSGIIDSQISKQALSEIEGRHKDIVRLESSIKE LHDMFMDIAMLVENQGEMLDNIELNVMHTVDHVEKAREETKRAVKYQGQARKKLVIIIVIVVVLLGILAL IIGLSVGLK -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
GALA, a Database for Genomic Sequence Alignments and Annotations
Resources GALA, a Database for Genomic Sequence Alignments and Annotations Belinda Giardine,1 Laura Elnitski,1,2 Cathy Riemer,1 Izabela Makalowska,4 Scott Schwartz,1 Webb Miller,1,3,4 and Ross C. Hardison2,4,5 Departments of 1Computer Science and Engineering, 2Biochemistry and Molecular Biology, 3Biology, and 4Huck Institute for Life Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA We have developed a relational database to contain whole genome sequence alignments between human and mouse with extensive annotations of the human sequence. Complex queries are supported on recorded features, both directlyand on proximityamong them. Searches can reveal a wide varietyof relationships, such as finding all genes expressed in a designated tissue that have a highlyconserved non coding sequence !Ј to the start site. Other examples are finding single nucleotide polymorphisms that occur in conserved noncoding regions upstream of genes and identifying CpG islands that overlap the !Ј ends of divergentlytranscribed genes. The database is available online at http://globin.cse.psu.edu/ and http://bio.cse.psu.edu/. The determination and annotation of complete genomic combined with sequence conservation can refine predictions DNA sequences provide the opportunity for unprecedented of functional sequences (Levy et al. 2001). One way to do this advances in our understanding of evolution, genetics, and is to record both extensive annotations and sequence align- physiology, but the amount and diversity of data pose daunt- ments in a database. ing challenges as well. Three excellent browsers provide access We have developed a database of genomic DNA se- to the sequence and annotations of the human genome, viz., quence alignments and annotations, called GALA, to search the human genome browser !"#$ at %&'& (Kent et al. -
ANSWER KEY Sybsc. Life Sciences- SEM
ANSWER KEY S.Y.B.Sc. Life Sciences- SEM III - Paper III Q.P.Code: 79543 Exam Date: 2nd November 2018 Marks : 100 Q. 1 Do as Directed: (20mks) .Q. 1. A) Define / Explain the following terms: (07) 1. TCP/IP- It is commonly known as TCP/IP because the foundational protocols in the suite are the Transmission Control Protocol (TCP) and the Internet Protocol (IP). It is a set of networking protocols that allows two or more computers to communicate. 2. WWW- The World Wide Web (WWW), also called the Web, is an information space where documents and other web resourcesare identified by Uniform Resource Locators (URLs), interlinked by hypertext links, and accessible via the Internet. Web pages are primarily text documents formatted and annotated with Hypertext Markup Language (HTML). In addition to formatted text, web pages may contain images, video, audio, and software components that are rendered in the user's web browser as coherent pages of multimedia content. 3. Proteomics- Proteomics is the large-scale study of proteomes. A proteome is a set of proteins produced in an organism, system, or biological context. 4. Human Genome Project-The Genome Project (HGP) was an international scientific research project with the goal of determining the sequence of nucleotide base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint. 5. Forward reading frame-An open reading frame starts with an atg (Met) in most species and ends with a stop codon (taa, tag or tga). -
"Phylogenetic Analysis of Protein Sequence Data Using The
Phylogenetic Analysis of Protein Sequence UNIT 19.11 Data Using the Randomized Axelerated Maximum Likelihood (RAXML) Program Antonis Rokas1 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee ABSTRACT Phylogenetic analysis is the study of evolutionary relationships among molecules, phenotypes, and organisms. In the context of protein sequence data, phylogenetic analysis is one of the cornerstones of comparative sequence analysis and has many applications in the study of protein evolution and function. This unit provides a brief review of the principles of phylogenetic analysis and describes several different standard phylogenetic analyses of protein sequence data using the RAXML (Randomized Axelerated Maximum Likelihood) Program. Curr. Protoc. Mol. Biol. 96:19.11.1-19.11.14. C 2011 by John Wiley & Sons, Inc. Keywords: molecular evolution r bootstrap r multiple sequence alignment r amino acid substitution matrix r evolutionary relationship r systematics INTRODUCTION the baboon-colobus monkey lineage almost Phylogenetic analysis is a standard and es- 25 million years ago, whereas baboons and sential tool in any molecular biologist’s bioin- colobus monkeys diverged less than 15 mil- formatics toolkit that, in the context of pro- lion years ago (Sterner et al., 2006). Clearly, tein sequence analysis, enables us to study degree of sequence similarity does not equate the evolutionary history and change of pro- with degree of evolutionary relationship. teins and their function. Such analysis is es- A typical phylogenetic analysis of protein sential to understanding major evolutionary sequence data involves five distinct steps: (a) questions, such as the origins and history of data collection, (b) inference of homology, (c) macromolecules, developmental mechanisms, sequence alignment, (d) alignment trimming, phenotypes, and life itself. -
Annotating a Non-Model Plant Genome – a Study on the Narrow-Leafed Lupin
BioTechnologia vol. 93(3) C pp. 318-332 C 2012 Journal of Biotechnology, Computational Biology and Bionanotechnology RESEARCH PAPER Annotating a non-model plant genome – a study on the narrow-leafed lupin ANDRZEJ ZIELEZIŃSKI 1, PIOTR POTARZYCKI 1, MICHAŁ KSIĄŻKIEWICZ 2, WOJCIECH M. KARŁOWSKI 1* 1 Laboratory of Computational Genomics, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland 2 Institute of Plant Genetics, Polish Academy of Sciences, Poznan, Poland * Corresponding author: [email protected] Abstract We present here a highly portable and easy-to-use gene annotation system CEL (Computational Environment for annotation of Legume genomes) that can be used to annotate any type of genomic sequence -- from BAC ends to complete chromosomes. CEL’s core engine is modular and hierarchically organized with an open-source struc- ture, permitting maximum customization -- users can assemble an individualized annotation pipeline by selecting computational components that best suit their annotation needs. The tool is designed to speed up genomic ana- lyses and features an algorithm that substitutes for a biologist’s expertise at various steps of gene structure pre- diction. This allows more complete automation of the labor-intensive and time-consuming annotation process. The system collects and prioritizes multiple sources of de novo gene predictions and gene expression evidence according to the confidence value of underlying supporting evidence, as a result producing high-quality gene- model sets. The data produced by CEL pipeline is suitable for direct visualization in any genome browser tool that supports GFF annotation format (e.g. Apollo, Artemis, Genome Browser etc.). This provides an easy means to view and edit individual contigs and BACs using just mouse’s clicks and drag-and-drop features. -
Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods
International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019 Performance Evaluation of Leading Protein Multiple Sequence Alignment Methods Arunima Mishra, B. K. Tripathi, S. S. Soam MSA is a well-known method of alignment of three or more Abstract: Protein Multiple sequence alignment (MSA) is a biological sequences. Multiple sequence alignment is a very process, that helps in alignment of more than two protein intricate problem, therefore, computation of exact MSA is sequences to establish an evolutionary relationship between the only feasible for the very small number of sequences which is sequences. As part of Protein MSA, the biological sequences are not practical in real situations. Dynamic programming as used aligned in a way to identify maximum similarities. Over time the sequencing technologies are becoming more sophisticated and in pairwise sequence method is impractical for a large number hence the volume of biological data generated is increasing at an of sequences while performing MSA and therefore the enormous rate. This increase in volume of data poses a challenge heuristic algorithms with approximate approaches [7] have to the existing methods used to perform effective MSA as with the been proved more successful. Generally, various biological increase in data volume the computational complexities also sequences are organized into a two-dimensional array such increases and the speed to process decreases. The accuracy of that the residues in each column are homologous or having the MSA is another factor critically important as many bioinformatics same functionality. Many MSA methods were developed over inferences are dependent on the output of MSA. -
HMMER User's Guide
HMMER User's Guide Biological sequence analysis using pro®le hidden Markov models http://hmmer.wustl.edu/ Version 2.1.1; December 1998 Sean Eddy Dept. of Genetics, Washington University School of Medicine 4566 Scott Ave., St. Louis, MO 63110, USA [email protected] With contributions by Ewan Birney ([email protected]) Copyright (C) 1992-1998, Washington University in St. Louis. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. The HMMER software package is a copyrighted work that may be freely distributed and modi®ed under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. Some versions of HMMER may have been obtained under specialized commercial licenses from Washington University; for details, see the ®les COPYING and LICENSE that came with your copy of the HMMER software. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Appendix for a copy of the full text of the GNU General Public License. 1 Contents 1 Tutorial 5 1.1 The programs in HMMER . 5 1.2 Files used in the tutorial . 6 1.3 Searching a sequence database with a single pro®le HMM . 6 HMM construction with hmmbuild . 7 HMM calibration with hmmcalibrate . 7 Sequence database search with hmmsearch . 8 Searching major databases like NR or SWISSPROT . -
Orthofiller: Utilising Data from Multiple Species to Improve the Completeness of Genome Annotations Michael P
Dunne and Kelly BMC Genomics (2017) 18:390 DOI 10.1186/s12864-017-3771-x SOFTWARE Open Access OrthoFiller: utilising data from multiple species to improve the completeness of genome annotations Michael P. Dunne and Steven Kelly* Abstract Backround: Complete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction. Results: Here we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species. Conclusions: We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species. Keywords: Genome annotation, Gene prediction, Orthology, Orthogroup Background of several effective algorithms for identifying genes in Genome sequences have become fundamental to many de novo sequenced genomes [3].