Andrew D. Warren

Bio: Andrew D. Warren is an academic researcher from Florida Museum of Natural History. The author has contributed to research in topics: Nymphalidae & Lepidoptera genitalia. The author has an hindex of 18, co-authored 67 publications receiving 2264 citations. Previous affiliations of Andrew D. Warren include National Autonomous University of Mexico & University of Florida.

Order Lepidoptera Linnaeus, 1758. In : Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness

Abstract: van Nieukerken, Erik J.; Kaila, Lauri; Kitching, Ian J.; Kristensen, Niels Peder; Lees, David C.; Minet, Joël; Mitter, Charles; Mutanen, Marko; Regier, Jerome C.; Simonsen, Thomas J.; Wahlberg, Niklas; Yen, Shen-Horn; Zahiri, Reza; Adamski, David; Baixeras, Joaquin; Bartsch, Daniel; Bengtsson, Bengt Å.; Brown, John W.; Bucheli, Sibyl Rae; Davis, Donald R.; de Prins, Jurate; de Prins, Willy; Epstein, Marc E.; Gentili-Poole, Patricia; Gielis, Caes; Hättenschwiler, Peter; Hausmann, Axel; Holloway, Jeremy D.; Kallies, Axel; Karsholt, Ole; Kawahara, Akito Y.; Koster, Sjaak; Kozlov, Mikhail; Lafontaine, J. Donald; Lamas, Gerardo; Landry, JeanFrançois; Lee, Sangmi; Nuss, Matthias; Park, Kyu-Tek; Penz, Carla; Rota, Jadranka; Schintlmeister, Alexander; Schmidt, B. Christian; Sohn, Jae-Cheon; Solis, M. Alma; Tarmann, Gerhard M.; Warren, Andrew D.; Weller, Susan; Yakovlev, Roman V.; Zolotuhin, Vadim V.; Zwick, Andreas

Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers

Abstract: Phylogenetic relationships among major clades of butterflies and skippers have long been controversial, with no general consensus even today. Such lack of resolution is a substantial impediment to using the otherwise well studied butterflies as a model group in biology. Here we report the results of a combined analysis of DNA sequences from three genes and a morphological data matrix for 57 taxa (3258 characters, 1290 parsimony informative) representing all major lineages from the three putative butterfly superfamilies (Hedyloidea, Hesperioidea and Papilionoidea), plus out-groups representing other ditrysian Lepidoptera families. Recently, the utility of morphological data as a source of phylogenetic evidence has been debated. We present the first well supported phylogenetic hypothesis for the butterflies and skippers based on a total-evidence analysis of both traditional morphological characters and new molecular characters from three gene regions (COI, EF-1a and wingless). All four data partitions show substantial hidden support for the deeper nodes, which emerges only in a combined analysis in which the addition of morphological data plays a crucial role. With the exception of Nymphalidae, the traditionally recognized families are found to be strongly supported monophyletic clades with the following relationships: (HesperiidaeC(PapilionidaeC(PieridaeC(NymphalidaeC(LycaenidaeCRiodinidae))))). Nymphalidae is recovered as a monophyletic clade but this clade does not have strong support. Lycaenidae and Riodinidae are sister groups with strong support and we suggest that the latter be given family rank. The position of Pieridae as the sister taxon to nymphalids, lycaenids and riodinids is supported by morphology and the EF-1a data but conflicted by the COI and wingless data. Hedylidae are more likely to be related to butterflies and skippers than geometrid moths and appear to be the sister group to PapilionoideaC Hesperioidea.

Revised classification of the family Hesperiidae (Lepidoptera: Hesperioidea) based on combined molecular and morphological data

Abstract: We propose a revised higher classification for the genera of Hesperiidae (skipper butterflies) of the world. We have augmented our published DNA data matrix with 49 morphological characters in order to infer relationships for taxa not sampled in the molecular study. We use the results of a combined analysis to identify morphological synapomorphies of the suprageneric clades of Hesperiidae, and to hypothesize a phylogenetic classification of the world’s genera of Hesperiidae, the first of its kind for this diverse group. Monophyly of the family Hesperiidae is strongly supported, as are some of the traditionally recognized subfamilies. The results presented here largely corroborate those of our molecular study, but differ in several details. The Australian endemic Euschemon rafflesia is given subfamily status, as is Eudaminae. We recognize seven subfamilies of Hesperiidae: Coeliadinae, Euschemoninae (confirmed status), Eudaminae (new status), Pyrginae, Heteropterinae (confirmed status), Trapezitinae and Hesperiinae. We treat Pyrrhopygini, Tagiadini, Celaenorrhinini, Carcharodini, Achlyodidini, Erynnini and Pyrgini as tribes of Pyrginae. Circumscriptions of Achlyodidini and Pyrgini require further elucidation. Tribes of Hesperiinae include Aeromachini, Baorini, Taractrocerini, Thymelicini, Calpodini (reinstated status), Anthoptini (new tribe), Moncini and Hesperiini. The tribal placement of many Old World hesperiine genera remains ambiguous.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation

Abstract: The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55,000 organisms (>4800 viruses, >40,000 prokaryotes and >10,000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Automated Eukaryotic Gene Structure Annotation Using EVidenceModeler and the Program to Assemble Spliced Alignments

Abstract: EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.