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The Human Transcriptome: An Unfinished Story
Mihaela Pertea
Genes , 2012, DOI: 10.3390/genes3030344
Abstract: Despite recent technological advances, the study of the human transcriptome is still in its early stages. Here we provide an overview of the complex human transcriptomic landscape, present the bioinformatics challenges posed by the vast quantities of transcriptomic data, and discuss some of the studies that have tried to determine how much of the human genome is transcribed. Recent evidence has suggested that more than 90% of the human genome is transcribed into RNA. However, this view has been strongly contested by groups of scientists who argued that many of the observed transcripts are simply the result of transcriptional noise. In this review, we conclude that the full extent of transcription remains an open question that will not be fully addressed until we decipher the complete range and biological diversity of the transcribed genomic sequences.
BREAST RECONSTRUCTION AFTER MASTECTOMY FOR CANCER. THE TECHNIQUE OF LATISSIMUS DORSI MUSCULOCUTANEOUS FLAP
Mihaela Pertea,S. Lunca
Jurnalul de Chirurgie , 2005,
Abstract: Breast conservation surgery is now widely accepted as the treatment of choice in breast cancer. The aim of immediate breast reconstruction is to improve well-being and quality of life for women undergoing mastectomy for breast cancer. Immediate breast reconstruction after mastectomy has increased over the past decade, following the unequivocal demonstration of its oncological safety and the availability of reliable methods of reconstruction. The use of latissimus dorsi musculocutaneous flap to replace the volume loss after major breast resection is an option where the tumour to breast volume ratio is large. The latissimus dorsi muscle flap is a simple and reliable technique for breast reconstruction and was first described in 1896. The latissimus dorsi flap is known to be a well-vascularized flap with minimal risk of fat necrosis. The main disadvantage of this procedure is a high rate of donor-site seroma. Comparing with other techniques of reconstruction, the technique of latissimus dorsi flap is simple and imply a few steps: marking the flap, raising the flap, anterior transposition of the flap, covering the defect and donor site closure. This procedure must be known by all surgeons (general, plastic and breast) involved in breast surgery. The aim of this article is to describe the technique of latissimus dorsi flap in breast reconstruction.
Do-it-yourself genetic testing
Steven L Salzberg, Mihaela Pertea
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-10-404
Abstract: As we learn more about the associations between genes and disease, a growing number of diagnostic tests have been developed to detect mutations that increase the risks of various diseases. However, anyone who wants to develop a diagnostic test or a treatment based on human genes faces a potential roadblock: gene patents. A 2005 study [1] reported that 4,382 human genes (~20% of the total number in our genome) are covered by patents or other intellectual property claims. These patents cover a wide range of methods for assaying the DNA sequence of an individual for the presence of disease-associated mutations. For example, one of the most consequential gene patents covers mutations in the BRCA1 [2] and BRCA2 [3] genes, which are associated with a significantly increased risk of breast and ovarian cancer [4-6]. The BRCA gene patents, which are held by Myriad Genetics, cover all known cancer-causing mutations in addition to those that might be discovered in the future. No one can develop a commercial diagnostic test or a treatment based on the BRCA gene sequences without a license from Myriad. Although a US federal court recently overturned seven of Myriad's BRCA patents, Myriad is appealing the ruling, and it holds 16 other BRCA-related patents that it claims are unaffected by the court's ruling [7].As the cost of DNA sequencing falls, the idea of testing for mutations one gene at a time is rapidly becoming obsolete. We are also rapidly approaching the day when it will be cheaper to fully sequence a genome before testing the sequence for all known genetic mutations associated with a given disease than to conduct multiple separate tests for each gene. Currently Myriad charges more than $3000 for its tests on the BRCA genes, while sequencing one's entire genome now costs less than $20,000. Furthermore, once an individual's genome has been sequenced, it becomes a resource that can be re-tested as new disease-causing mutations are discovered.In contrast to whole-genome seq
Between a chicken and a grape: estimating the number of human genes
Mihaela Pertea, Steven L Salzberg
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-s1-i1
Abstract:
Between a chicken and a grape: estimating the number of human genes
Mihaela Pertea, Steven L Salzberg
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-5-206
Abstract: Ever since the discovery of the genetic code, scientists have been trying to catalog all the genes in the human genome. Over the years, the best estimate of the number of human genes has grown steadily smaller, but we still do not have an accurate count. Here we review the history of efforts to establish the human gene count and present the current best estimates.The first attempt to estimate the number of genes in the human genome appeared more than 45 years ago, while the genetic code was still being deciphered. Friedrich Vogel published his 'preliminary estimate' in 1964 [1], based on the number of amino acids in the alpha- and beta-chains of hemoglobin (141 and 146, respectively). Knowing that three nucleotides corresponded to each amino acid, he extrapolated to compute the molecular weight of the DNA comprising these genes. He then made several assumptions in order to produce his estimate: that these proteins were typical in size (they are actually smaller than average); that nucleotide sequences were uninterrupted on the chromosomes (introns were discovered more than 10 years later [2,3]); and that the entire genome was protein coding. All these assumptions were reasonable at the time, but later discoveries would reveal that none of them was correct. Vogel then used the molecular weight of the human haploid chromosomes to correctly calculate the genome size as 3 × 109 nucleotides, and dividing that by the size of a 'typical' gene, came up with an estimate of 6.7 million genes.Even at the time, Vogel found this number 'disturbingly high', but no one suspected in 1964 that most human genes were interrupted by multiple introns, nor did anyone know that vast regions of the human genome would turn out to contain seemingly meaningless repetitive sequences. Since Vogel's initial attempt, many scientists have tried to estimate the number of genes in the human genome, using increasingly sophisticated molecular tools. Over the years, the number has gradually come down,
Detection of lineage-specific evolutionary changes among primate species
Mihaela Pertea, Geo M Pertea, Steven L Salzberg
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-274
Abstract: We have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection.DivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.The genome of a living species is the product of a long series of changes, including neutral, beneficial, and detrimental alterations to the sequence. Sequence changes that affect the organism's fitness are subject to evolutionary pressures, such as the pressure to survive, to out-compete other species, and to defend the organism against external attack. In order to uncover these changes, we need to know what the ancestral genome looked like, which we can infer by comparing multiple genomes to one another. As we accumulate genomes from species related to human, and especially from within the primate lineages, we should be able to learn more about what makes humans special. At the same time, we can learn what makes each primate different from the others. Until recently, methods for detecting the effects of evolution had been
A computational survey of candidate exonic splicing enhancer motifs in the model plant Arabidopsis thaliana
Mihaela Pertea, Stephen M Mount, Steven L Salzberg
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-159
Abstract: We have developed a new computational technique to identify significantly conserved motifs involved in splice site regulation. First, 84 putative exonic splicing enhancer hexamers are identified in Arabidopsis thaliana. Then a Gibbs sampling program called ELPH was used to locate conserved motifs represented by these hexamers in exonic regions near splice sites in confirmed genes. Oligomers containing 35 of these motifs have been shown experimentally to induce significant inclusion of A. thaliana exons. Second, integration of our regulatory motifs into two different splice site recognition programs significantly improved the ability of the software to correctly predict splice sites in a large database of confirmed genes. We have released GeneSplicerESE, the improved splice site recognition code, as open source software.Our results show that the use of the ESE motifs consistently improves splice site prediction accuracy.Alternative splicing is an important regulatory mechanism for many species, allowing them to generate multiple variant proteins from the same primary transcript. In order to predict the complete protein complement of any eukaryote, we need to detect alternative splice sites and put them together in the correct combinations. Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions [1]. However the sequence conservation found at the splice site junctions is not strong enough to accurately differentiate between introns and exons [2]. Additional sequences, residing at variable distances from splice sites, have been shown to function as cis-acting factor binding sites that regulate splicing either in vivo or in vitro. Although such splicing regulators have been identified in both exons and introns, exonic splicing regulators (ESRs) are generally better characterized, and are probably more common [3,4]. Such ESRs either enhance or suppress the utilizat
JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions
Jonathan E Allen, William H Majoros, Mihaela Pertea, Steven L Salzberg
Genome Biology , 2006, DOI: 10.1186/gb-2006-7-s1-s9
Abstract: Here we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy.While in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based 'combiner' program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research.Predicting complete protein-coding genes in human DNA remains a significant challenge, as the results of the ENCODE Genome Annotation Assessment Project (EGASP) workshop clearly demonstrate. Although much progress has been made of late in the use of increasingly sophisticated models of gene structure, particularly those that utilize homology evidence within a phylogenetic framework (for example, [1,2]), it is clear that there is yet much room for improvement. In the wake of the most recent spate of advances in gene structure modeling, we additionally observe that the sophistication in modeling techniques has to some degree outstripped our ability to ascribe, with high confidence, specific reasons for the difference in performance between competing gene finding systems, particularly those that utilize similar underlying models and/or forms of evidence, but that differ
Efficient decoding algorithms for generalized hidden Markov model gene finders
William H Majoros, Mihaela Pertea, Arthur L Delcher, Steven L Salzberg
BMC Bioinformatics , 2005, DOI: 10.1186/1471-2105-6-16
Abstract: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN.In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques.Generalized Hidden Markov Models have seen wide use in recent years in the field of computational gene prediction. A number of ab initio gene-finding programs are now available which utilize this mathematical framework internally for the modeling and evaluation of gene structure [1-6], and newer systems are now emerging which expand this framework by simultaneously modeling two genomes at once, in order to harness the mutually informative signals present in homologous gene structures from recently diverged species. As greater numbers of such genomes become available, it is tempting to consider the possibility of integrating all this information into increasingly complex models of gene structure and evolution.Notwithstanding our eagerness to utilize this expected flood of genomic data, methods have yet to be demonstrated which can perform such large-scale parallel analyses without requiring inordinate computational resources. In the case of Generalized Pair HMMs (GPHMMs), for example, the only systems in existence of which we are familiar make
Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments
Brian J Haas, Steven L Salzberg, Wei Zhu, Mihaela Pertea, Jonathan E Allen, Joshua Orvis, Owen White, C Robin Buell, Jennifer R Wortman
Genome Biology , 2008, DOI: 10.1186/gb-2008-9-1-r7
Abstract: Accurate and comprehensive gene discovery in eukaryotic genome sequences requires multiple independent and complementary analysis methods including, at the very least, the application of ab initio gene prediction software and sequence alignment tools. The problem is technically challenging, and despite many years of research no single method has yet been able to solve it, although numerous tools have been developed to target specialized and diverse variations on the gene finding problem (for review [1,2]). Conventional gene finding software employs probabilistic techniques such as hidden Markov models (HMMs). These models are employed to find the most likely partitioning of a nucleotide sequence into introns, exons, and intergenic states according to a prior set of probabilities for the states in the model. Such gene finding programs, including GENSCAN [3], GlimmerHMM [4], Fgenesh [5], and GeneMark.hmm [6], are effective at identifying individual exons and regions that correspond to protein-coding genes, but nevertheless they are far from perfect at correctly predicting complete gene structures, differing from correct gene structures in exon content or position [7-10].The correct gene structures, or individual components including introns and exons, are often apparent from spliced alignments of homologous transcript or protein sequences. Many software tools are available that perform these alignment tasks. Tools used to align expressed sequence tags (ESTs) and full-length cDNAs (FL-cDNAs) to genomic sequence include EST_GENOME [11], AAT [12], sim4 [13], geneseqer [14], BLAT [15], and GMAP [16], among numerous others. The list of programs that perform spliced alignments of protein sequences to DNA are much fewer, including the multifunctional AAT, exonerate [17], and PMAP (derived from GMAP). An extension of spliced protein alignment that includes a probabilistic model of eukaryotic gene structure is implemented in GeneWise [18], a popular homology-based gene predict
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