(Below N is a link to NCBI taxonomic web page and E link to ESTHER at designed phylum.) > cellular organisms: NE > Eukaryota: NE > Opisthokonta: NE > Metazoa: NE > Eumetazoa: NE > Bilateria: NE > Protostomia: NE > Ecdysozoa: NE > Panarthropoda: NE > Arthropoda: NE > Mandibulata: NE > Pancrustacea: NE > Hexapoda: NE > Insecta: NE > Dicondylia: NE > Pterygota: NE > Neoptera: NE > Holometabola: NE > Diptera: NE > Nematocera: NE > Culicomorpha: NE > Culicoidea: NE > Culicidae: NE > Anophelinae: NE > Anopheles [genus]: NE > Nyssorhynchus: NE > argyritarsis section: NE > argyritarsis series: NE > darlingi group: NE > Anopheles darlingi: NE
LegendThis sequence has been compared to family alignement (MSA) red => minority aminoacid blue => majority aminoacid color intensity => conservation rate title => sequence position(MSA position)aminoacid rate Catalytic site Catalytic site in the MSA MVMRRVRHIDPAIQLAAQQGCAASRVMVKVAQGFIYGTKDRLPNGQPYYC FKGVPYAQPPIGRLRFASPVPIEKYSVTYLDCSSERSACLGRDVITREIT GSEDGLFLNVYTPRIGKEVDPAAEATLLPVIVFFHGGGMTGGNADSGLYL PDYLVQEGVIVVTVNYRLGVLGFLCLPQAGIEGNAGLKDQRMALQWVQQN IRVFGGDRENVTLAGASSGGTNVVMHCFSDQSRRLFHKAIAQSGTVFADL VYQTEPEERARSLARLFGYEGTSDEAVLNTLRDVPARRLCEAQFLVLSPR QREYESIFHFPFTTVIERPESVDAVLRRTPVEYLRERDHFTMPVLCGYND RDGMLELIDMMQHLAVYNERPEKLICASFDVDYFSPEARALGEELKRHYL GGEPIARGNISPVVDLLTDRFLVGFYVLCQLWHQHQTKVPMYAYRFAYDG SLNKSKDLLKFRDLAGACHIDEVYYLFSSALLRTEVAPGDPAYQMRRTMV QMWTNFAKYSDPTPPHDERLTVRWKPMEAITDGEGNTNYTLLSIGNGGLK MDTMPELGRMGKYLELLRRCNGAIDQFPIPKAPVAQQEQREDTKAGS
Anopheles darlingi is the principal neotropical malaria vector, responsible for more than a million cases of malaria per year on the American continent. Anopheles darlingi diverged from the African and Asian malaria vectors approximately 100 million years ago (mya) and successfully adapted to the New World environment. Here we present an annotated reference A. darlingi genome, sequenced from a wild population of males and females collected in the Brazilian Amazon. A total of 10 481 predicted protein-coding genes were annotated, 72% of which have their closest counterpart in Anopheles gambiae and 21% have highest similarity with other mosquito species. In spite of a long period of divergent evolution, conserved gene synteny was observed between A. darlingi and A. gambiae. More than 10 million single nucleotide polymorphisms and short indels with potential use as genetic markers were identified. Transposable elements correspond to 2.3% of the A. darlingi genome. Genes associated with hematophagy, immunity and insecticide resistance, directly involved in vector-human and vector-parasite interactions, were identified and discussed. This study represents the first effort to sequence the genome of a neotropical malaria vector, and opens a new window through which we can contemplate the evolutionary history of anopheline mosquitoes. It also provides valuable information that may lead to novel strategies to reduce malaria transmission on the South American continent. The A. darlingi genome is accessible at www.labinfo.lncc.br/index.php/anopheles-darlingi.
Title: Combination of measures distinguishes pre-miRNAs from other stem-loops in the genome of the newly sequenced Anopheles darlingi Mendes ND, Freitas AT, Vasconcelos AT, Sagot MF Ref: BMC Genomics, 11:529, 2010 : PubMed
BACKGROUND: Efforts using computational algorithms towards the enumeration of the full set of miRNAs of an organism have been limited by strong reliance on arguments of precursor conservation and feature similarity. However, miRNA precursors may arise anew or be lost across the evolutionary history of a species and a newly sequenced genome may be evolutionarily too distant from other genomes for an adequate comparative analysis. In addition, the learning of intricate classification rules based purely on features shared by miRNA precursors that are currently known may reflect a perpetuating identification bias rather than a sound means to tell true miRNAs from other genomic stem-loops. RESULTS: We show that there is a strong bias amongst annotated pre-miRNAs towards robust stem-loops in the genomes of Drosophila melanogaster and Anopheles gambiae and we propose a scoring scheme for precursor candidates which combines four robustness measures. Additionally, we identify several known pre-miRNA homologs in the newly sequenced Anopheles darlingi and show that most are found amongst the top-scoring precursor candidates. Furthermore, a comparison of the performance of our approach is made against two single-genome pre-miRNA classification methods. CONCLUSIONS: In this paper we present a strategy to sieve through the vast amount of stem-loops found in metazoan genomes in search of pre-miRNAs, significantly reducing the set of candidates while retaining most known miRNA precursors. This approach makes no use of conservation data and relies solely on properties derived from our knowledge of miRNA biogenesis.
