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                    "doi": "10.1002/rcm.6162",
                    "pmid": "22368051",
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                    "note": null,
                    "metadata": {
                        "title": "Molecular phylogenetics by direct comparison of tandem mass spectra",
                        "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.",
                        "date": "2012-04-15T00:00:00Z",
                        "citationCount": 26,
                        "authors": [
                            {
                                "name": "Palmblad M."
                            },
                            {
                                "name": "Deelder A.M."
                            }
                        ],
                        "journal": "Rapid Communications in Mass Spectrometry"
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                {
                    "doi": "10.1021/acs.jproteome.2c00457",
                    "pmid": "36173614",
                    "pmcid": "PMC9903320",
                    "type": [
                        "Other"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets",
                        "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.",
                        "date": "2023-02-03T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Marissen R."
                            },
                            {
                                "name": "Varunjikar M.S."
                            },
                            {
                                "name": "Laros J.F.J."
                            },
                            {
                                "name": "Rasinger J.D."
                            },
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
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                        "journal": "Journal of Proteome Research"
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                {
                    "doi": "10.1021/acs.jproteome.1c00528",
                    "pmid": "34523928",
                    "pmcid": "PMC8491155",
                    "type": [
                        "Review"
                    ],
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                    "note": null,
                    "metadata": {
                        "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics",
                        "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.",
                        "date": "2021-10-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
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                        "journal": "Journal of Proteome Research"
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                                    "term": "ClustalW format"
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                            ]
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2884",
                                "term": "Plot"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3603",
                                    "term": "PNG"
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                                    "uri": "http://edamontology.org/format_2331",
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                    "note": "Other Input formats:\nAMSA (.amsa);\nJnetFile (.concise, .jnet);\nPFAM (.pfam);\nSubstitution matrix (.matrix);\nJalview Project File (.jvp);\nJalview Feature File (.features, .jvfeatures);\nJalview Annotations File (.annotations, .jvannotations);\n\n...\nOther Output formats:\nPFAM (.pfam);\nBioJS (.biojs) (interactive HTML/Javascript);\nJalview Project File (.jvp);",
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                        "Repository"
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                    "note": null
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                {
                    "url": "https://twitter.com/Jalview",
                    "type": [
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                    "note": "Twitter feed"
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                    "url": "https://www.youtube.com/channel/UCIjpnvZB770yz7ftbrJ0tfw",
                    "type": [
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                    "note": "Hands-on exercises, Training courses and Training videos"
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                {
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                    "metadata": {
                        "title": "Jalview Version 2-A multiple sequence alignment editor and analysis workbench",
                        "abstract": "Summary: Jalview Version 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments. Core features include keyboard and mouse-based editing, multiple views and alignment overviews, and linked structure display with Jmol. Jalview 2 is available in two forms: a lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server. © 2009 The Author(s).",
                        "date": "2009-05-07T00:00:00Z",
                        "citationCount": 5999,
                        "authors": [
                            {
                                "name": "Waterhouse A.M."
                            },
                            {
                                "name": "Procter J.B."
                            },
                            {
                                "name": "Martin D.M.A."
                            },
                            {
                                "name": "Clamp M."
                            },
                            {
                                "name": "Barton G.J."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jim Procter",
                    "email": null,
                    "url": "http://www.lifesci.dundee.ac.uk/people/jim-procter",
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        {
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                            "term": "Data handling"
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                            "term": "Sequence editing"
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                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0850",
                                "term": "Sequence set"
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                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
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                    "term": "Nucleic acids"
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                    "note": null,
                    "metadata": {
                        "title": "Bioinformatics Protocols for Quickly Obtaining Large-Scale Data Sets for Phylogenetic Inferences",
                        "abstract": "Useful insight into the evolution of genes and gene families can be provided by the analysis of all available genome datasets rather than just a few, which are usually those of model species. Handling and transforming such datasets into the desired format for downstream analyses is, however, often a difficult and time-consuming task for researchers without a background in informatics. Therefore, we present two simple and fast protocols for data preparation, using an easy-to-install, open-source, cross-platform software application with user-friendly, rich graphical user interface (SEDA; http://www.sing-group.org/seda/index.html). The first protocol is a substantial improvement over one recently published (López-Fernández et al. Practical applications of computational biology and bioinformatics, 12th International conference. Springer, Cham, pp 88–96 (2019)[1]), which was used to study the evolution of GULO, a gene that encodes the enzyme responsible for the last step of vitamin C synthesis. In this paper, we show how the sequence data file used for the phylogenetic analyses can now be obtained much faster by changing the way coding sequence isoforms are removed, using the newly implemented SEDA operation “Remove isoforms”. This protocol can be used to easily show that putative functional GULO genes are present in several Prostotomian groups such as Molluscs, Priapulida and Arachnida. Such findings could have been easily missed if only a few Protostomian model species had been used. The second protocol allowed us to identify positively selected amino acid sites in a set of 19 primate HLA immunity genes. Interestingly, the proteins encoded by MHC class II genes can show just as many positively selected amino acid sites as those encoded by classical MHC class I genes. Although a significant percentage of codons, which can be as high as 14.8%, are evolving under positive selection, the main mode of evolution of HLA immunity genes is purifying selection. Using a large number of primate species, the probability of missing the identification of positively selected amino acid sites is lower. Both projects were performed in less than one week, and most of the time was spent running the analyses rather than preparing the files. Such protocols can be easily adapted to answer many other questions using a phylogenetic approach.",
                        "date": "2019-03-01T00:00:00Z",
                        "citationCount": 9,
                        "authors": [
                            {
                                "name": "Lopez-Fernandez H."
                            },
                            {
                                "name": "Duque P."
                            },
                            {
                                "name": "Henriques S."
                            },
                            {
                                "name": "Vazquez N."
                            },
                            {
                                "name": "Fdez-Riverola F."
                            },
                            {
                                "name": "Vieira C.P."
                            },
                            {
                                "name": "Reboiro-Jato M."
                            },
                            {
                                "name": "Vieira J."
                            }
                        ],
                        "journal": "Interdisciplinary Sciences – Computational Life Sciences"
                    }
                },
                {
                    "doi": "10.1109/TCBB.2020.3040383",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.5.1",
                    "note": null,
                    "metadata": {
                        "title": "SEDA: A Desktop Tool Suite for FASTA Files Processing",
                        "abstract": "SEDA (SEquence DAtaset builder) is a multiplatform desktop application for the manipulation of FASTA files containing DNA or protein sequences. The convenient graphical user interface gives access to a collection of simple (filtering, sorting, or file reformatting, among others) and advanced (BLAST searching, protein domain annotation, gene annotation, and sequence alignment) utilities not present in similar applications, which eases the work of life science researchers working with DNA and/or protein sequences, especially those who have no programming skills. This paper presents general guidelines on how to build efficient data handling protocols using SEDA, as well as practical examples on how to prepare high-quality datasets for single gene phylogenetic studies, the characterization of protein families, or phylogenomic studies. The user-friendliness of SEDA also relies on two important features: (i) the availability of easy-to-install distributable versions and installers of SEDA, including a Docker image for Linux, and (ii) the facility with which users can manage large datasets. SEDA is open-source, with GNU General Public License v3.0 license, and publicly available at GitHub (https://github.com/sing-group/seda). SEDA installers and documentation are available at https://www.sing-group.org/seda/.",
                        "date": "2022-01-01T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Lopez-Fernandez H."
                            },
                            {
                                "name": "Duque P."
                            },
                            {
                                "name": "Vazquez N."
                            },
                            {
                                "name": "Fdez-Riverola F."
                            },
                            {
                                "name": "Reboiro-Jato M."
                            },
                            {
                                "name": "Vieira C.P."
                            },
                            {
                                "name": "Vieira J."
                            }
                        ],
                        "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
                    }
                }
            ],
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        {
            "name": "PCASuite",
            "description": "Allows the user to compress Molecular Dynamics (MD) trajectories using Principal Component Analysis (PCA) algorithms. This technique offers a good compression ratio at the expense of losing some precision in the trajectory.",
            "homepage": "http://mmb.pcb.ub.edu/software/pcasuite/",
            "biotoolsID": "pcasuite",
            "biotoolsCURIE": "biotools:pcasuite",
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            "function": [
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0244",
                            "term": "Protein flexibility and motion analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0883",
                                "term": "Structure"
                            },
                            "format": []
                        }
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0883",
                                "term": "Structure"
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                            "format": []
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
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                    "uri": "http://edamontology.org/topic_0176",
                    "term": "Molecular dynamics"
                },
                {
                    "uri": "http://edamontology.org/topic_2269",
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                "Windows",
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            "download": [
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                    "url": "http://mmb.pcb.ub.es/software/pcasuite/",
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                {
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                {
                    "url": "https://anaconda.org/bioconda/pcasuite",
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                    "note": null,
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            ],
            "documentation": [
                {
                    "url": "http://mmb.pcb.ub.es/software/pcasuite/pcasuite.html",
                    "type": [
                        "General"
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            ],
            "publication": [
                {
                    "doi": "10.1021/ct050285b",
                    "pmid": "26626512",
                    "pmcid": null,
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                    "metadata": {
                        "title": "Essential dynamics: A tool for efficient trajectory compression and management",
                        "abstract": "We present a simple method for compression and management of very large molecular dynamics trajectories. The approach is based on the projection of the Cartesian snapshots collected along the trajectory into an orthogonal space defined by the eigenvectors obtained by diagonalization of the covariance matrix. The transformation is mathematically exact when the number of eigenvectors equals 3N-6 (N being the number of atoms), and in practice very accurate even when the number of eigenvectors is much smaller, permitting a dramatic reduction in the size of trajectory files. In addition, we have examined the ability of the method, when combined with interpolation, to recover dense samplings (snapshots collected at a high frequency) from more sparse (lower frequency) data as a method for further data compression. Finally, we have investigated the possibility of using the approach when extrapolating the behavior of the system to times longer than the original simulation period. Overall our results suggest that the method is an attractive alternative to current approaches for including dynamic information in static structure files such as those deposited in the Protein Data Bank. © 2006 American Chemical Society.",
                        "date": "2006-01-01T00:00:00Z",
                        "citationCount": 91,
                        "authors": [
                            {
                                "name": "Meyer T."
                            },
                            {
                                "name": "Ferrer-Costa C."
                            },
                            {
                                "name": "Perez A."
                            },
                            {
                                "name": "Rueda M."
                            },
                            {
                                "name": "Bidon-Chanal A."
                            },
                            {
                                "name": "Luque F.J."
                            },
                            {
                                "name": "Laughton C.A."
                            },
                            {
                                "name": "Orozco M."
                            }
                        ],
                        "journal": "Journal of Chemical Theory and Computation"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Institute for Research in Biomedicine Barcelona",
                    "email": "adam.hospital@irbbarcelona.org",
                    "url": null,
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            "owner": "jdianes",
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        },
        {
            "name": "LolliPop",
            "description": "Tool for Deconvolution for Wastewater Genomics - The LolliPop package comprises a python module and an optional set of command-line tools using it to build curves of viral variants over time  within environmental sample. It runs a  kernel-based deconvolution and leverages the time series nature of the samples.",
            "homepage": "https://github.com/cbg-ethz/LolliPop",
            "biotoolsID": "lollipop",
            "biotoolsCURIE": "biotools:lollipop",
            "version": [],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "v-pipe",
                    "type": "usedBy"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0554",
                            "term": "Allele frequency distribution analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Nucleic acid sequence alignment"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2523",
                                "term": "Phylogenetic data"
                            },
                            "format": []
                        }
                    ],
                    "note": "Use 'aln2basecnt' from smallgenome utilities for (simple, unfiltered) SNV calling into basecount.tsv\nUse 'xsv' to concatenate all samples into the input of the deconvolution",
                    "cmd": "lollipop generate-mutlist --output mutlist.tsv --out-pangovars variants_pangolin.yaml --genes Genes_NC_045512.2.GFF3 -- vocs/delta_mutations_full.yaml vocs/omicron_ba1_mutations_full.yaml vocs/omicron_ba2_mutations_full.yaml\naln2basecnt --first 1 --basecnt sample1.basecnt.tsv.gz --coverage sample1.coverage.tsv.gz --name \"sample1\" sample1.bam\nlollipop getmutations from-basecount --based 1 --output sample1.mut.tsv --location \"main plant\" --date \"2023-02-27\" -m mutlist.tsv -- sample1.basecnt.tsv.gz\nxsv cat rows --output tallymut.tsv sample*.mut.tsv\nlollipop deconvolution --output=deconvoluted.tsv --out-json=deconvoluted_upload.json --var=variants_conf.yaml --vd=variants_dates.yaml --dec=deconv_linear.yaml --seed=42 -- tallymut.tsv"
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                }
            ],
            "operatingSystem": [
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