Comparative analysis of nuclear localization signal (NLS) prediction methods

Aim. Comparative analysis of six state-of-the-art nuclear localization signal (NLS) prediction methods (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport and seqNLS). Methods. Each program was tested for correct predictions using a dataset of 155 experimentally determined NLSs and for false-positives using a dataset of 155 transmembrane proteins, which putatively lack NLS. Results. The most suitable NLS predictors wer fond to be NucPred, NLStradamus and seqNLS; these programs provide the maximum rate of correct to wrong predictions among the tested programs. However, the best results obtained by these programs were only ~ 45 % of the correct predictions. Conclusion. The identification of novel NLSs by predictors still requires experimental verification.Мета. Ідентифікація сигналів ядерної локалізації (NLS) в амінокислотній послідовності білків за допомогою експериментальних методів залишається коштовним і тривалис процесом. Тому в останній час велику популярність отримали комп'ютерні методи прогнозування NLS. Методи. В даній статті ми провели порівняльний аналіз достовірності прогнозування NLS шести різних програм (PSORT II, ​​NucPred, cNLSMapper, NLStradamus, NucImport та SeqNLS). Для кожного алгоритма було оцінена доля істинно позитивних прогнозів на вибірки з 155 експериментально визначених NLS з 128 білків людини, а також частку помилкових подій у вибірці з 155 трансмембранних білків людини, які, як видно, позбавлені NLS. Результати. Найбільшу кількість вірно прогнозованих NLS при найменшій частці хибнопозитивних результатів було отримано для трьох програм: NucPred, NLStradamus та seqNLS. Однак навіть при набільшій ступені достовірності дані алгоритми прогнозують вірно не більше 45 % експериментально визначених NLS. Висновки. Використання будь-яких алгоритмів прогнозування NLS вимагає експериментальної перевірки отриманих результатів.Цель. Идентификация сигналов ядерной локализации (NLS) в аминокислотной последовательности белка экспериментальными методами остается дорогостоящим и долгим процессом. Поэтому в последнее время большую популярность получили компьютерные методы предсказания NLS. Методы. В данной статье мы провели сравнительный анализ достоверности предсказания NLS шести различных программ (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport и SeqNLS). Для каждого алгоритма была оценена доля истинно положительных предсказаний на выборке из 155 экспериментально определенных NLS из 128 человеческих белков, а также доля ложноположительных предсказаний на выборке из 155 трансмембранных белков человека, которые, предположительно, лишены NLS. Наибольшее количество правильно предсказанных NLS при наименьшей доле ложноположительных результатов было получено для трех программ: NucPred, NLStradamus и seqNLS. Однако даже при наибольшей степени достоверности данные алгоритмы предсказывают правильно не более 45% экспериментально определенных NLS, т.е. использование любых алгоритмов предсказания NLS требует экспериментальной проверки получаемых результатов

Population sequencing data reveal a compendium of mutational processes in human germline

Mechanistic processes underlying human germline mutations remain largely unknown.Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage

Publisher Correction:Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double-strand-break incidence (Nature Genetics, (2018), 50, 4, (487-492), 10.1038/s41588-018-0071-6)

In the HTML version of the article originally published, the figures for Supplementary Figures 1–15 were incorrect and did not match the correct figures in the PDF of Supplementary Text and Figures. The error has been corrected in the HTML version of the article

APOBEC3 Mediates Resistance to Oncolytic Viral Therapy

Tumor cells frequently evade applied therapies through the accumulation of genomic mutations and rapid evolution. In the case of oncolytic virotherapy, understanding the mechanisms by which cancer cells develop resistance to infection and lysis is critical to the development of more effective viral-based platforms. Here, we identify APOBEC3 as an important factor that restricts the potency of oncolytic vesicular stomatitis virus (VSV). We show that VSV infection of B16 murine melanoma cells upregulated APOBEC3 in an IFN-β-dependent manner, which was responsible for the evolution of virus-resistant cell populations and suggested that APOBEC3 expression promoted the acquisition of a virus-resistant phenotype. Knockdown of APOBEC3 in B16 cells diminished their capacity to develop resistance to VSV infection in vitro and enhanced the therapeutic effect of VSV in vivo. Similarly, overexpression of human APOBEC3B promoted the acquisition of resistance to oncolytic VSV both in vitro and in vivo. Finally, we demonstrate that APOBEC3B expression had a direct effect on the fitness of VSV, an RNA virus that has not previously been identified as restricted by APOBEC3B. This research identifies APOBEC3 enzymes as key players to target in order to improve the efficacy of viral or broader nucleic acid-based therapeutic platforms

Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

: O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s disease Z. Hu, P. Wang, Y. Zhu, J. Zhao, M. Xiong, David A Bennett O11 A microRNA signature identifies subtypes of triple-negative breast cancer and reveals MIR-342-3P as regulator of a lactate metabolic pathway A. Hidalgo-Miranda, S. Romero-Cordoba, S. Rodriguez-Cuevas, R. Rebollar-Vega, E. Tagliabue, M. Iorio, E. D’Ippolito, S. Baroni O12 Transcriptome analysis identifies genes, enhancer RNAs and repetitive elements that are recurrently deregulated across multiple cancer types B. Kaczkowski, Y. Tanaka, H. Kawaji, A. Sandelin, R. Andersson, M. Itoh, T. Lassmann, the FANTOM5 consortium, Y. Hayashizaki, P. Carninci, A. R. R. Forrest O13 Elevated mutation and widespread loss of constraint at regulatory and architectural binding sites across 11 tumour types C. A. Semple O14 Exome sequencing provides evidence of pathogenicity for genes implicated in colorectal cancer E. A. Rosenthal, B. Shirts, L. Amendola, C. Gallego, M. Horike-Pyne, A. Burt, P. Robertson, P. Beyers, C. Nefcy, D. Veenstra, F. Hisama, R. Bennett, M. Dorschner, D. Nickerson, J. Smith, K. Patterson, D. Crosslin, R. Nassir, N. Zubair, T. Harrison, U. Peters, G. Jarvik, NHLBI GO Exome Sequencing Project O15 The tandem duplicator phenotype as a distinct genomic configuration in cancer F. Menghi, K. Inaki, X. Woo, P. Kumar, K. Grzeda, A. Malhotra, H. Kim, D. Ucar, P. Shreckengast, K. Karuturi, J. Keck, J. Chuang, E. T. Liu O16 Modeling genetic interactions associated with molecular subtypes of breast cancer B. Ji, A. Tyler, G. Ananda, G. Carter O17 Recurrent somatic mutation in the MYC associated factor X in brain tumors H. Nikbakht, M. Montagne, M. Zeinieh, A. Harutyunyan, M. Mcconechy, N. Jabado, P. Lavigne, J. Majewski O18 Predictive biomarkers to metastatic pancreatic cancer treatment J. B. Goldstein, M. Overman, G. Varadhachary, R. Shroff, R. Wolff, M. Javle, A. Futreal, D. Fogelman O19 DDIT4 gene expression as a prognostic marker in several malignant tumors L. Bravo, W. Fajardo, H. Gomez, C. Castaneda, C. Rolfo, J. A. Pinto O20 Spatial organization of the genome and genomic alterations in human cancers K. C. Akdemir, L. Chin, A. Futreal, ICGC PCAWG Structural Alterations Group O21 Landscape of targeted therapies in solid tumors S. Patterson, C. Statz, S. Mockus O22 Genomic analysis reveals novel drivers and progression pathways in skin basal cell carcinoma S. N. Nikolaev, X. I. Bonilla, L. Parmentier, B. King, F. Bezrukov, G. Kaya, V. Zoete, V. Seplyarskiy, H. Sharpe, T. McKee, A. Letourneau, P. Ribaux, K. Popadin, N. Basset-Seguin, R. Ben Chaabene, F. Santoni, M. Andrianova, M. Guipponi, M. Garieri, C. Verdan, K. Grosdemange, O. Sumara, M. Eilers, I. Aifantis, O. Michielin, F. de Sauvage, S. Antonarakis O23 Identification of differential biomarkers of hepatocellular carcinoma and cholangiocarcinoma via transcriptome microarray meta-analysis S. Likhitrattanapisal O24 Clinical validity and actionability of multigene tests for hereditary cancers in a large multi-center study S. Lincoln, A. Kurian, A. Desmond, S. Yang, Y. Kobayashi, J. Ford, L. Ellisen O25 Correlation with tumor ploidy status is essential for correct determination of genome-wide copy number changes by SNP array T. L. Peters, K. R. Alvarez, E. F. Hollingsworth, D. H. Lopez-Terrada O26 Nanochannel based next-generation mapping for interrogation of clinically relevant structural variation A. Hastie, Z. Dzakula, A. W. Pang, E. T. Lam, T. Anantharaman, M. Saghbini, H. Cao, BioNano Genomics O27 Mutation spectrum in a pulmonary arterial hypertension (PAH) cohort and identification of associated truncating mutations in TBX4 C. Gonzaga-Jauregui, L. Ma, A. King, E. Berman Rosenzweig, U. Krishnan, J. G. Reid, J. D. Overton, F. Dewey, W. K. Chung O28 NORTH CAROLINA macular dystrophy (MCDR1): mutations found affecting PRDM13 K. Small, A. DeLuca, F. Cremers, R. A. Lewis, V. Puech, B. Bakall, R. Silva-Garcia, K. Rohrschneider, M. Leys, F. S. Shaya, E. Stone O29 PhenoDB and genematcher, solving unsolved whole exome sequencing data N. L. Sobreira, F. Schiettecatte, H. Ling, E. Pugh, D. Witmer, K. Hetrick, P. Zhang, K. Doheny, D. Valle, A. Hamosh O30 Baylor-Johns Hopkins Center for Mendelian genomics: a four year review S. N. Jhangiani, Z. Coban Akdemir, M. N. Bainbridge, W. Charng, W. Wiszniewski, T. Gambin, E. Karaca, Y. Bayram, M. K. Eldomery, J. Posey, H. Doddapaneni, J. Hu, V. R. Sutton, D. M. Muzny, E. A. Boerwinkle, D. Valle, J. R. Lupski, R. A. Gibbs O31 Using read overlap assembly to accurately identify structural genetic differences in an ashkenazi jewish trio S. Shekar, W. Salerno, A. English, A. Mangubat, J. Bruestle O32 Legal interoperability: a sine qua non for international data sharing A. Thorogood, B. M. Knoppers, Global Alliance for Genomics and Health - Regulatory and Ethics Working Group O33 High throughput screening platform of competent sineups: that can enhance translation activities of therapeutic target H. Takahashi, K. R. Nitta, A. Kozhuharova, A. M. Suzuki, H. Sharma, D. Cotella, C. Santoro, S. Zucchelli, S. Gustincich, P. Carninci O34 The undiagnosed diseases network international (UDNI): clinical and laboratory research to meet patient needs J. J. Mulvihill, G. Baynam, W. Gahl, S. C. Groft, K. Kosaki, P. Lasko, B. Melegh, D. Taruscio O36 Performance of computational algorithms in pathogenicity predictions for activating variants in oncogenes versus loss of function mutations in tumor suppressor genes R. Ghosh, S. Plon O37 Identification and electronic health record incorporation of clinically actionable pharmacogenomic variants using prospective targeted sequencing S. Scherer, X. Qin, R. Sanghvi, K. Walker, T. Chiang, D. Muzny, L. Wang, J. Black, E. Boerwinkle, R. Weinshilboum, R. Gibbs O38 Melanoma reprogramming state correlates with response to CTLA-4 blockade in metastatic melanoma T. Karpinets, T. Calderone, K. Wani, X. Yu, C. Creasy, C. Haymaker, M. Forget, V. Nanda, J. Roszik, J. Wargo, L. Haydu, X. Song, A. Lazar, J. Gershenwald, M. Davies, C. Bernatchez, J. Zhang, A. Futreal, S. Woodman O39 Data-driven refinement of complex disease classification from integration of heterogeneous functional genomics data in GeneWeaver E. J. Chesler, T. Reynolds, J. A. Bubier, C. Phillips, M. A. Langston, E. J. Baker O40 A general statistic framework for genome-based disease risk prediction M. Xiong, L. Ma, N. Lin, C. Amos O41 Integrative large-scale causal network analysis of imaging and genomic data and its application in schizophrenia studies N. Lin, P. Wang, Y. Zhu, J. Zhao, V. Calhoun, M. Xiong O42 Big data and NGS data analysis: the cloud to the rescue O. Dobretsberger, M. Egger, F. Leimgruber O43 Cpipe: a convergent clinical exome pipeline specialised for targeted sequencing S. Sadedin, A. Oshlack, Melbourne Genomics Health Alliance O44 A Bayesian classification of biomedical images using feature extraction from deep neural networks implemented on lung cancer data V. A. A. Antonio, N. Ono, Clark Kendrick C. Go O45 MAV-SEQ: an interactive platform for the Management, Analysis, and Visualization of sequence data Z. Ahmed, M. Bolisetty, S. Zeeshan, E. Anguiano, D. Ucar O47 Allele specific enhancer in EPAS1 intronic regions may contribute to high altitude adaptation of Tibetans C. Zeng, J. Shao O48 Nanochannel based next-generation mapping for structural variation detection and comparison in trios and populations H. Cao, A. Hastie, A. W. Pang, E. T. Lam, T. Liang, K. Pham, M. Saghbini, Z. Dzakula O49 Archaic introgression in indigenous populations of Malaysia revealed by whole genome sequencing Y. Chee-Wei, L. Dongsheng, W. Lai-Ping, D. Lian, R. O. Twee Hee, Y. Yunus, F. Aghakhanian, S. S. Mokhtar, C. V. Lok-Yung, J. Bhak, M. Phipps, X. Shuhua, T. Yik-Ying, V. Kumar, H. Boon-Peng O50 Breast and ovarian cancer prevention: is it time for population-based mutation screening of high risk genes? I. Campbell, M.-A. Young, P. James, Lifepool O53 Comprehensive coverage from low DNA input using novel NGS library preparation methods for WGS and WGBS C. Schumacher, S. Sandhu, T. Harkins, V. Makarov O54 Methods for large scale construction of robust PCR-free libraries for sequencing on Illumina HiSeqX platform H. DoddapaneniR. Glenn, Z. Momin, B. Dilrukshi, H. Chao, Q. Meng, B. Gudenkauf, R. Kshitij, J. Jayaseelan, C. Nessner, S. Lee, K. Blankenberg, L. Lewis, J. Hu, Y. Han, H. Dinh, S. Jireh, K. Walker, E. Boerwinkle, D. Muzny, R. Gibbs O55 Rapid capture methods for clinical sequencing J. Hu, K. Walker, C. Buhay, X. Liu, Q. Wang, R. Sanghvi, H. Doddapaneni, Y. Ding, N. Veeraraghavan, Y. Yang, E. Boerwinkle, A. L. Beaudet, C. M. Eng, D. M. Muzny, R. A. Gibbs O56 A diploid personal human genome model for better genomes from diverse sequence data K. C. C. Worley, Y. Liu, D. S. T. Hughes, S. C. Murali, R. A. Harris, A. C. English, X. Qin, O. A. Hampton, P. Larsen, C. Beck, Y. Han, M. Wang, H. Doddapaneni, C. L. Kovar, W. J. Salerno, A. Yoder, S. Richards, J. Rogers, J. R. Lupski, D. M. Muzny, R. A. Gibbs O57 Development of PacBio long range capture for detection of pathogenic structural variants Q. Meng, M. Bainbridge, M. Wang, H. Doddapaneni, Y. Han, D. Muzny, R. Gibbs O58 Rhesus macaques exhibit more non-synonymous variation but greater impact of purifying selection than humans R. A. Harris, M. Raveenedran, C. Xue, M. Dahdouli, L. Cox, G. Fan, B. Ferguson, J. Hovarth, Z. Johnson, S. Kanthaswamy, M. Kubisch, M. Platt, D. Smith, E. Vallender, R. Wiseman, X. Liu, J. Below, D. Muzny, R. Gibbs, F. Yu, J. Rogers O59 Assessing RNA structure disruption induced by single-nucleotide variation J. Lin, Y. Zhang, Z. Ouyang P1 A meta-analysis of genome-wide association studies of mitochondrial dna copy number A. Moore, Z. Wang, J. Hofmann, M. Purdue, R. Stolzenberg-Solomon, S. Weinstein, D. Albanes, C.-S. Liu, W.-L. Cheng, T.-T. Lin, Q. Lan, N. Rothman, S. Berndt P2 Missense polymorphic genetic combinations underlying down syndrome susceptibility E. S. Chen P4 The evaluation of alteration of ELAM-1 expression in the endometriosis patients H. Bahrami, A. Khoshzaban, S. Heidari Keshal P5 Obesity and the incidence of apolipoprotein E polymorphisms in an assorted population from Saudi Arabia population K. K. R. Alharbi P6 Genome-associated personalized antithrombotical therapy for patients with high risk of thrombosis and bleeding M. Zhalbinova, A. Akilzhanova, S. Rakhimova, M. Bekbosynova, S. Myrzakhmetova P7 Frequency of Xmn1 polymorphism among sickle cell carrier cases in UAE population M. Matar P8 Differentiating inflammatory bowel diseases by using genomic data: dimension of the problem and network organization N. Mili, R. Molinari, Y. Ma, S. Guerrier P9 Vulnerability of genetic variants to the risk of autism among Saudi children N. Elhawary, M. Tayeb, N. Bogari, N. Qotb P10 Chromatin profiles from ex vivo purified dopaminergic neurons establish a promising model to support studies of neurological function and dysfunction S. A. McClymont, P. W. Hook, L. A. Goff, A. McCallion P11 Utilization of a sensitized chemical mutagenesis screen to identify genetic modifiers of retinal dysplasia in homozygous Nr2e3rd7 mice Y. Kong, J. R. Charette, W. L. Hicks, J. K. Naggert, L. Zhao, P. M. Nishina P12 Ion torrent next generation sequencing of recessive polycystic kidney disease in Saudi patients B. M. Edrees, M. Athar, F. A. Al-Allaf, M. M. Taher, W. Khan, A. Bouazzaoui, N. A. Harbi, R. Safar, H. Al-Edressi, A. Anazi, N. Altayeb, M. A. Ahmed, K. Alansary, Z. Abduljaleel P13 Digital expression profiling of Purkinje neurons and dendrites in different subcellular compartments A. Kratz, P. Beguin, S. Poulain, M. Kaneko, C. Takahiko, A. Matsunaga, S. Kato, A. M. Suzuki, N. Bertin, T. Lassmann, R. Vigot, P. Carninci, C. Plessy, T. Launey P14 The evolution of imperfection and imperfection of evolution: the functional and functionless fractions of the human genome D. Graur P16 Species-independent identification of known and novel recurrent genomic entities in multiple cancer patients J. Friis-Nielsen, J. M. Izarzugaza, S. Brunak P18 Discovery of active gene modules which are densely conserved across multiple cancer types reveal their prognostic power and mutually exclusive mutation patterns B. S. Soibam P19 Whole exome sequencing of dysplastic leukoplakia tissue indicates sequential accumulation of somatic mutations from oral precancer to cancer D. Das, N. Biswas, S. Das, S. Sarkar, A. Maitra, C. Panda, P. Majumder P21 Epigenetic mechanisms of carcinogensis by hereditary breast cancer genes J. J. Gruber, N. Jaeger, M. Snyder P22 RNA direct: a novel RNA enrichment strategy applied to transcripts associated with solid tumors K. Patel, S. Bowman, T. Davis, D. Kraushaar, A. Emerman, S. Russello, N. Henig, C. Hendrickson P23 RNA sequencing identifies gene mutations for neuroblastoma K. Zhang P24 Participation of SFRP1 in the modulation of TMPRSS2-ERG fusion gene in prostate cancer cell lines M. Rodriguez-Dorantes, C. D. Cruz-Hernandez, C. D. P. Garcia-Tobilla, S. Solorzano-Rosales P25 Targeted Methylation Sequencing of Prostate Cancer N. Jäger, J. Chen, R. Haile, M. Hitchins, J. D. Brooks, M. Snyder P26 Mutant TPMT alleles in children with acute lymphoblastic leukemia from México City and Yucatán, Mexico S. Jiménez-Morales, M. Ramírez, J. Nuñez, V. Bekker, Y. Leal, E. Jiménez, A. Medina, A. Hidalgo, J. Mejía P28 Genetic modifiers of Alström syndrome J. Naggert, G. B. Collin, K. DeMauro, R. Hanusek, P. M. Nishina P31 Association of genomic variants with the occurrence of angiotensin-converting-enzyme inhibitor (ACEI)-induced coughing among Filipinos E. M. Cutiongco De La Paz, R. Sy, J. Nevado, P. Reganit, L. Santos, J. D. Magno, F. E. Punzalan , D. Ona , E. Llanes, R. L. Santos-Cortes , R. Tiongco, J. Aherrera, L. Abrahan, P. Pagauitan-Alan; Philippine Cardiogenomics Study Group P32 The use of “humanized” mouse models to validate disease association of a de novo GARS variant and to test a novel gene therapy strategy for Charcot-Marie-Tooth disease type 2D K. H. Morelli, J. S. Domire, N. Pyne, S. Harper, R. Burgess P34 Molecular regulation of chondrogenic human induced pluripotent stem cells M. A. Gari, A. Dallol, H. Alsehli, A. Gari, M. Gari, A. Abuzenadah P35 Molecular profiling of hematologic malignancies: implementation of a variant assessment algorithm for next generation sequencing data analysis and clinical reporting M. Thomas, M. Sukhai, S. Garg, M. Misyura, T. Zhang, A. Schuh, T. Stockley, S. Kamel-Reid P36 Accessing genomic evidence for clinical variants at NCBI S. Sherry, C. Xiao, D. Slotta, K. Rodarmer, M. Feolo, M. Kimelman, G. Godynskiy, C. O’Sullivan, E. Yaschenko P37 NGS-SWIFT: a cloud-based variant analysis framework using control-accessed sequencing data from DBGAP/SRA C. Xiao, E. Yaschenko, S. Sherry P38 Computational assessment of drug induced hepatotoxicity through gene expression profiling C. Rangel-Escareño, H. Rueda-Zarate P40 Flowr: robust and efficient pipelines using a simple language-agnostic approach;ultraseq; fast modular pipeline for somatic variation calling using flowr S. Seth, S. Amin, X. Song, X. Mao, H. Sun, R. G. Verhaak, A. Futreal, J. Zhang P41 Applying “Big data” technologies to the rapid analysis of heterogenous large cohort data S. J. Whiite, T. Chiang, A. English, J. Farek, Z. Kahn, W. Salerno, N. Veeraraghavan, E. Boerwinkle, R. 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Polfus, CHARGE and NHLBI Exome Sequence Project Working Groups P48 Association of adipoq gene with type 2 diabetes and related phenotypes in african american men and women: The jackson heart study S. Davis, R. Xu, S. Gebeab, P Riestra, A Gaye, R. Khan, J. Wilson, A. Bidulescu P49 Common variants in casr gene are associated with serum calcium levels in koreans S. H. Jung, N. Vinayagamoorthy, S. H. Yim, Y. J. Chung P50 Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with multiple exponential functions Y. Zhou, S. Xu P51 A Bayesian framework for generalized linear mixed models in genome-wide association studies X. Wang, V. Philip, G. Carter P52 Targeted sequencing approach for the identification of the genetic causes of hereditary hearing impairment A. A. Abuzenadah, M. Gari, R. Turki, A. Dallol P53 Identification of enhancer sequences by ATAC-seq open chromatin profiling A. Uyar, A. Kaygun, S. Zaman, E. Marquez, J. George, D. 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Comparative analysis of nuclear localization signal (NLS) prediction methods

Aim. Comparative analysis of six state-of-the-art nuclear localization signal (NLS) prediction methods (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport and seqNLS). Methods. Each program was tested for correct predictions using a dataset of 155 experimentally determined NLSs and for false-positives using a dataset of 155 transmembrane proteins, which putatively lack NLS. Results. The most suitable NLS predictors wer fond to be NucPred, NLStradamus and seqNLS; these programs provide the maximum rate of correct to wrong predictions among the tested programs. However, the best results obtained by these programs were only ~ 45 % of the correct predictions. Conclusion. The identification of novel NLSs by predictors still requires experimental verification.Мета. Ідентифікація сигналів ядерної локалізації (NLS) в амінокислотній послідовності білків за допомогою експериментальних методів залишається коштовним і тривалис процесом. Тому в останній час велику популярність отримали комп'ютерні методи прогнозування NLS. Методи. В даній статті ми провели порівняльний аналіз достовірності прогнозування NLS шести різних програм (PSORT II, ​​NucPred, cNLSMapper, NLStradamus, NucImport та SeqNLS). Для кожного алгоритма було оцінена доля істинно позитивних прогнозів на вибірки з 155 експериментально визначених NLS з 128 білків людини, а також частку помилкових подій у вибірці з 155 трансмембранних білків людини, які, як видно, позбавлені NLS. Результати. Найбільшу кількість вірно прогнозованих NLS при найменшій частці хибнопозитивних результатів було отримано для трьох програм: NucPred, NLStradamus та seqNLS. Однак навіть при набільшій ступені достовірності дані алгоритми прогнозують вірно не більше 45 % експериментально визначених NLS. Висновки. Використання будь-яких алгоритмів прогнозування NLS вимагає експериментальної перевірки отриманих результатів.Цель. Идентификация сигналов ядерной локализации (NLS) в аминокислотной последовательности белка экспериментальными методами остается дорогостоящим и долгим процессом. Поэтому в последнее время большую популярность получили компьютерные методы предсказания NLS. Методы. В данной статье мы провели сравнительный анализ достоверности предсказания NLS шести различных программ (PSORT II, NucPred, cNLSMapper, NLStradamus, NucImport и SeqNLS). Для каждого алгоритма была оценена доля истинно положительных предсказаний на выборке из 155 экспериментально определенных NLS из 128 человеческих белков, а также доля ложноположительных предсказаний на выборке из 155 трансмембранных белков человека, которые, предположительно, лишены NLS. Наибольшее количество правильно предсказанных NLS при наименьшей доле ложноположительных результатов было получено для трех программ: NucPred, NLStradamus и seqNLS. Однако даже при наибольшей степени достоверности данные алгоритмы предсказывают правильно не более 45% экспериментально определенных NLS, т.е. использование любых алгоритмов предсказания NLS требует экспериментальной проверки получаемых результатов