Genome assembly and annotation of the tambaqui (Colossoma macropomum): an emblematic fish of the Amazon River basin
TL;DR: The first high-quality genome for the Colossoma macropomum known as "tambaqui" was presented in this paper. But it was not used for aquaculture applications.
Abstract: Colossoma macropomum known as “tambaqui” is the largest Characiformes fish in the Amazon River Basin and a leading species in Brazilian aquaculture and fisheries. Good quality meat and great adaptability to culture systems are some of its remarkable farming features. To support studies into the genetics and genomics of the tambaqui, we have produced the first high-quality genome for the species. We combined Illumina and PacBio sequencing technologies to generate a reference genome, assembled with 39X coverage of long reads and polished to a QV=36 with 130X coverage of short reads. The genome was assembled into 1,269 scaffolds to a total of 1,221,847,006 bases, with a scaffold N50 size of 40 Mb where 93% of all assembled bases were placed in the largest 54 scaffolds that corresponds to the diploid karyotype of the tambaqui. Furthermore, the NCBI Annotation Pipeline annotated genes, pseudogenes, and non-coding transcripts using the RefSeq database as evidence, guaranteeing a high-quality annotation. A Genome Data Viewer for the tambaqui was produced which benefits any groups interested in exploring unique genomic features of the species. The availability of a highly accurate genome assembly for tambaqui provides the foundation for novel insights about ecological and evolutionary facets and is a helpful resource for aquaculture purposes.
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TL;DR: In this paper , the full repertoire of very long-chain fatty acids (Elovl) proteins are identified in the tambaqui Colossoma macropomum genome and a detailed phylogenetic and synteny analysis suggests a conservation of these genes among teleosts.
Abstract: Elongation of very long-chain fatty acids (Elovl) proteins are critical players in the regulation of the length of a fatty acid. At present, eight members of the Elovl family (Elovl1-8), displaying a characteristic fatty acid substrate specificity, have been identified in vertebrates, including teleost fish. In general, Elovl1, Elovl3, Elovl6 and Elovl7 exhibit a substrate preference for saturated and monounsaturated fatty acids, while Elovl2, Elovl4, Elovl5 and Elovl8 use polyunsaturated fatty acids (PUFA) as substrates. PUFA elongases have received considerable attention in aquatic animals due to their involvement in the conversion of C18 PUFAs to long-chain polyunsaturated fatty acids (LC-PUFA). Here, we identified the full repertoire of elovl genes in the tambaqui Colossoma macropomum genome. A detailed phylogenetic and synteny analysis suggests a conservation of these genes among teleosts. Furthermore, based on RNAseq gene expression data, we discovered a gender bias expression of elovl genes during sex differentiation of tambaqui, toward future males. Our findings suggest a role of Elovl enzymes and fatty acid metabolism in tambaqui sexual differentiation.
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TL;DR: In this article , the authors developed a dense linkage map for chromosome-level scaffolding and investigated the genetic architecture of resistance to A. hydrophila in tambaqui in a genome-wide association study (GWAS).
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References
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TL;DR: This version of MAFFT has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update.
Abstract: We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
27,771 citations
TL;DR: A new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves and a new test to assess the support of the data for internal branches of a phylogeny are introduced.
Abstract: PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
14,385 citations
TL;DR: Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements.
Abstract: We created a visualization tool called Circos to facilitate the identification and analysis of similarities and differences arising from comparisons of genomes. Our tool is effective in displaying variation in genome structure and, generally, any other kind of positional relationships between genomic intervals. Such data are routinely produced by sequence alignments, hybridization arrays, genome mapping, and genotyping studies. Circos uses a circular ideogram layout to facilitate the display of relationships between pairs of positions by the use of ribbons, which encode the position, size, and orientation of related genomic elements. Circos is capable of displaying data as scatter, line, and histogram plots, heat maps, tiles, connectors, and text. Bitmap or vector images can be created from GFF-style data inputs and hierarchical configuration files, which can be easily generated by automated tools, making Circos suitable for rapid deployment in data analysis and reporting pipelines.
8,315 citations
TL;DR: TrimAl is a tool for automated alignment trimming, which is especially suited for large-scale phylogenetic analyses and can automatically select the parameters to be used in each specific alignment so that the signal-to-noise ratio is optimized.
Abstract: Multiple sequence alignments (MSA) are central to many areas of bioinformatics, including phylogenetics, homology modeling, database searches and motif finding. Recently, such MSA-based techniques have been incorporated in high-throughput pipelines such as genome annotation and phylogenomics analyses. In all these applications, the reliability and accuracy of the analyses depend critically on the quality of the underlying alignments. A plethora of computer programs and algorithms for MSA are currently available (Notredame, 2007), which implement different heuristics to find mathematically optimal solutions to the MSA problem. Accuracies of 80–90% have been reported for the best algorithms, but even the best scoring alignment algorithms may fail with certain protein families or at specific regions in the alignment. The situation worsens in large-scale analyses, where faster but less reliable algorithms and large numbers of automatically selected sequences are used. It is therefore generally assumed that trimming the alignment, so that poorly aligned regions are eliminated, increases the accuracy of the resulting MSA-based applications (Talavera and Castresana, 2007). Some programs such as G-blocks (Castresana, 2000) have been developed to assist in the MSA trimming phase by selecting blocks of conserved regions. They have become very popular and are extensively used, with good performance, in small-to-medium scale datasets, where several parameters can be tested manually (Talavera and Castresana, 2007). However, their use over larger datasets is hampered by the need for defining, prior to the analysis, the set of parameters that will be used for all sequence families. Here, we present trimAl, a tool for automated alignment trimming. Its speed and the possibility for automatically adjusting the parameters to improve the phylogenetic signal-to-noise ratio, makes trimAl especially suited for large-scale phylogenomic analyses, involving thousands of large alignments.
trimAl has been developed in a GNU/Linux environment using C++ programming language and has been tested on various UNIX, Mac and Windows platforms. Moreover, we have developed a web server to run trimAl online (http://phylemon2.bioinfo.cipf.es/), which has been included in the Phylemon suite for phylogenetic and phylogenomic tools (Tarraga et al., 2007). The documentation, source files and additional information for trimAl are available through a wiki page (http://trimal.cgenomics.org).
trimAl reads and renders protein or nucleotide alignments in several standard formats. trimAl starts by reading all columns in an alignment and computes a score (Sx) for each of them. This score can be a gap score (Sg), a similarity score (Ss) or a consistency score (Sc). The score for each column can be computed based only on the information from that column or, if a window size of w is specified, it corresponds to the average value of w columns around the position considered.
The gap score (Sg) for a column is the fraction of sequences without a gap in that position. The residue similarity score (Ss) consists of mean distance (MD) scores as described in Thompson et al. (2001) and Supplementary Material. This score uses the MD between pairs of residues, as defined by a given scoring matrix. Finally, the consistency score (Sc) can only be computed when more than one alignment for the same set of sequences is provided. Details on how these scores are computed are provided in the Supplementary Material. In brief, Sc measures the level of consistency of all the residue pairs found in a column as compared with the other alignments. The alignment with the highest consistency is chosen and then trimmed to remove the columns that are less conserved, according to Sc or other thresholds set by the user.
Once all column scores have been computed trimAl can proceed in two ways. If both a score and a minimum conservation threshold are provided, trimAl renders a trimmed alignment in which only the columns with scores above the score threshold are included, as far as the number of selected columns is above a conservation threshold defined by the user. If this number is below the conservation threshold, trimAl will add more columns to the trimmed alignment in a decreasing order of scores until the conservation threshold is reached. The conservation threshold corresponds to the minimum percentage of columns, from the original alignment, which the user wants to include in the trimmed alignment.
Alternatively, if the automatic selection of parameters options is selected, trimAl will compute specific score thresholds depending on the inherent characteristics of each alignment. So far, trimAl incorporates three modes for the automated selection of parameters, gappyout, strict and strictplus, which are based on the different use of gap and similarity scores. Moreover, the option automated1 implements a heuristic to decide the most appropriate mode depending on the alignment characteristics. The heuristics to define such parameters have been designed based on the results of a benchmark. Details on the heuristics and the benchmark can be found in the online documentation of the program. In brief, the automatic selection of parameters approximate optimal cutoffs by plotting, internally, the cumulative graphs of gap and similarity scores of the columns in the alignment (see online documentation).
We expanded, using ROSE simulations (Stoye et al., 1998) a benchmark set that has been used previously to test the improvement in phylogenetic performance after an alignment trimming phase (Talavera and Castresana, 2007). This dataset simulates several evolutionary scenarios varying in the number and length of the sequences, the topology of the underlying tree and the level of sequence divergence considered. We compared the results obtained from MUSCLE alignments before and after trimming with trimAl using automated selection of parameters. The accuracy of the resulting trees was measured by comparing them with the original trees used to generate the sequence sets, and measuring the Robinson Foulds distance (Robinson and Foulds, 1981). We observed an overall improvement of the phylogenetic accuracy after trimming. Using -automated1 option of trimAl, the trimmed alignment always produced Maximum Likelihood trees that were of equal (36%) or significantly better (64%) quality as compared with the tree derived from the complete alignment. For Neighbor Joining reconstruction the -strictplus option of trimAl worked best, improving the phylogenetic accuracy in 89% of the scenarios. In most scenarios (90%), trimAl outperformed Gblocks v0.91b with default parameters. Most importantly, the use of Gblocks default parameters diminished the accuracy of the subsequent tree reconstruction in half of the scenarios considered. In contrast, the use of trimAl automated methods rarely (1.5%) undermined the topological accuracy of the resulting phylogenetic tree (see Supplementary Material for more details).
To test the applicability of trimAl on real datasets as well as its suitability for large-scale phylogenetic datasets, we ran trimAl on the complete set of MUSCLE alignments generated for the Human Phylome project (Huerta-Cepas et al., 2007). This includes a total of 31 182 alignments, containing, on average, 67 sequences of 1472 positions of length. Trimming these alignments using the -gappyout and automated1 options used 5 min 45 s and 125 min, 2 s, respectively, on a computer with an Intel QuadCore XEON E5410 processors and 8 GB of RAM.
trimAl has been used previously in a pipeline to reconstruct complete collections of gene trees. In this case, the parameter sets used were a minimum conservation threshold of 60% and a gap threshold of 90% (-cons 60 -gt 0.9). Complete and trimmed alignments used to generate the phylomes included in PhylomeDB (Huerta-Cepas et al., 2008) can be viewed through this database.
6,807 citations
TL;DR: Pilon is a fully automated, all-in-one tool for correcting draft assemblies and calling sequence variants of multiple sizes, including very large insertions and deletions, which is being used to improve the assemblies of thousands of new genomes and to identify variants from thousands of clinically relevant bacterial strains.
Abstract: Advances in modern sequencing technologies allow us to generate sufficient data to analyze hundreds of bacterial genomes from a single machine in a single day. This potential for sequencing massive numbers of genomes calls for fully automated methods to produce high-quality assemblies and variant calls. We introduce Pilon, a fully automated, all-in-one tool for correcting draft assemblies and calling sequence variants of multiple sizes, including very large insertions and deletions. Pilon works with many types of sequence data, but is particularly strong when supplied with paired end data from two Illumina libraries with small e.g., 180 bp and large e.g., 3-5 Kb inserts. Pilon significantly improves draft genome assemblies by correcting bases, fixing mis-assemblies and filling gaps. For both haploid and diploid genomes, Pilon produces more contiguous genomes with fewer errors, enabling identification of more biologically relevant genes. Furthermore, Pilon identifies small variants with high accuracy as compared to state-of-the-art tools and is unique in its ability to accurately identify large sequence variants including duplications and resolve large insertions. Pilon is being used to improve the assemblies of thousands of new genomes and to identify variants from thousands of clinically relevant bacterial strains. Pilon is freely available as open source software.
5,659 citations