191 research outputs found
Support vector machine for classification of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae based on codon composition
BACKGROUND: Meiotic double-strand breaks occur at relatively high frequencies in some genomic regions (hotspots) and relatively low frequencies in others (coldspots). Hotspots and coldspots are receiving increasing attention in research into the mechanism of meiotic recombination. However, predicting hotspots and coldspots from DNA sequence information is still a challenging task. RESULTS: We present a novel method for classification of hot and cold ORFs located in hotspots and coldspots respectively in Saccharomyces cerevisiae, using support vector machine (SVM), which relies on codon composition differences. This method has achieved a high classification accuracy of 85.0%. Since codon composition is a fusion of codon usage bias and amino acid composition signals, the ability of these two kinds of sequence attributes to discriminate hot ORFs from cold ORFs was also investigated separately. Our results indicate that neither codon usage bias nor amino acid composition taken separately performed as well as codon composition. Moreover, our SVM based method was applied to the full genome: We predicted the hot/cold ORFs from the yeast genome by using cutoffs of recombination rate. We found that the performance of our method for predicting cold ORFs is not as good as that for predicting hot ORFs. Besides, we also observed a considerable correlation between meiotic recombination rate and amino acid composition of certain residues, which probably reflects the structural and functional dissimilarity between the hot and cold groups. CONCLUSION: We have introduced a SVM-based novel method to discriminate hot ORFs from cold ones. Applying codon composition as sequence attributes, we have achieved a high classification accuracy, which suggests that codon composition has strong potential to be used as sequence attributes in the prediction of hot and cold ORFs
Exonuclease III protection assay with FRET probe for detecting DNA-binding proteins
We describe a new method for the assay of sequence-specific DNA-binding proteins in this paper. In this method, the sensitive fluorescence resonance energy transfer (FRET) technology is combined with the common DNA footprinting assay in order to develop a simple, rapid and high-throughput approach for quantitatively detecting the sequence-specific DNA-binding proteins. We named this method as exonuclease III (ExoIII) protection assay with FRET probe. The FRET probe used in this assay was a duplex DNA which was designed to contain one FRET pair in the center and two flanking protein-binding sites. During protein detection, if a target protein exists, it will bind to the two protein-binding sites of the FRET probe and thus protect the FRET pair from ExoIII digestion, resulting in high FRET. However, if the target protein does not exist, the FRET pair on the naked FRET probe will be degraded by ExoIII, resulting in low FRET. Three kinds of recombinant transcription factors including NF-κB, SP1 and p50, and the target protein of NF-κB in HeLa cell nuclear extracts, were successfully detected by the assay. This assay can be extensively used in biomedical research targeted at DNA-binding proteins
Next-Generation Sequencing of MicroRNAs for Breast Cancer Detection
It is reported that different microRNA (miRNA) profiles can be detected in the blood of cancer patients. We investigated that whether the key serum miRNAs could discriminate patients with and without breast cancer. This study was divided into three parts: (1) miRNA marker discovery using SOLiD sequencing-based miRNA profiling on cancerous and adjacent noncancerous breast tissue of one breast cancer patient; (2) marker selection and validation by real-time PCR on a small set of serum; (3) gene ontology analysis of the key miRNA target genes. Of genome-wide tissue miRNA expression analysis, five miRNAs were found to be altered more than fivefold by SOLiD sequencing (i.e., miR-29a, miR-23a, miR-23b, miR-192, and miR-21). All the five miRNAs were validated on the 20 breast cancer patients and 20 controls. miR-29a and miR-21 were significantly increased in the serum of breast cancer patients (P < .05). Gene ontology analysis of the target genes revealed enrichment for special biological process categories, that is, signal transduction, development, apoptosis, cell proliferation, and cell adhesion. SOLiD sequencing provides a promising method for cancer-related miRNA profiling. Serum miRNAs may be useful biomarkers for breast cancer detection
Haplotype Distribution and Evolutionary Pattern of miR-17 and miR-124 Families Based on Population Analysis
Background: MicroRNAs (miRNAs) are small, endogenously expressed non-coding RNAs that regulate mRNAs posttranscriptionally. Previous studies have explored miRNA evolutionary trend, but evolutionary history and pattern in the miRNA world are still not fully clear. In the paper, we intended to analyze miRNA haplotype distribution and evolutionary network by analyzing miRNA sequences of miR-17 and miR-124 families across animal species as special populations. Principal Findings: 31 haplotypes were detected in miR-17 family while only 9 haplotypes were defined in miR-124 family. The complex miR-17 family was mainly distributed in vertebrates, but miR-124 was shared by more animal species from Caenorhabditis to Homo and had a wide distribution spectrum. Some haplotypes of the two miRNA families appeared discontinuous distributions across animals. Compared with a simple phylogenetic network in miR-124 family, miR-17 family indicated a complex network with some median vectors that might be lost ancestral or potential miRNA haplotypes. By analyzing different miRNAs across 12 animal species, we found these small RNAs showed different haplotype diversities, haplotype distributions and phylogenetic networks. Conclusions: Different miRNAs had quite different haplotype distributions and evolutionary patterns. Discontinuous distributions of miRNAs and median vectors in phylogenetic networks implied more members in the miRNA world. miRN
MRP2 and GSTP1 polymorphisms and chemotherapy response in advanced non-small cell lung cancer
MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one
A novel method to quantify local CpG methylation density by regional methylation elongation assay on microarray
<p>Abstract</p> <p>Background</p> <p>DNA methylation based techniques are important tools in both clinical diagnostics and therapeutics. But most of these methods only analyze a few CpG sites in a target region. Indeed, difference of site-specific methylation may also lead to a change of methylation density in many cases, and it has been found that the density of methylation is more important than methylation of single CpG site for gene silencing.</p> <p>Results</p> <p>We have developed a novel approach for quantitative analysis of CpG methylation density on the basis of microarray-based hybridization and incorporation of Cy5-dCTP into the Cy3 labeled target DNA by using Taq DNA Polymerase on microarray. The quantification is achieved by measuring Cy5/Cy3 signal ratio which is proportional to methylation density. This methylation-sensitive technique, termed RMEAM (regional methylation elongation assay on microarray), provides several advantages over existing methods used for methylation analysis. It can determine an exact methylation density of the given region, and has potential of high throughput. We demonstrate a use of this method in determining the methylation density of the promoter region of the tumor-related gene <it>MLH1, TERT </it>and <it>MGMT </it>in colorectal carcinoma patients.</p> <p>Conclusion</p> <p>This technique allows for quantitative analysis of regional methylation density, which is the representative of all allelic methylation patterns in the sample. The results show that this technique has the characteristics of simplicity, rapidness, specificity and high-throughput.</p
Pair-barcode high-throughput sequencing for large-scale multiplexed sample analysis
<p>Abstract</p> <p>Background</p> <p>The multiplexing becomes the major limitation of the next-generation sequencing (NGS) in application to low complexity samples. Physical space segregation allows limited multiplexing, while the existing barcode approach only permits simultaneously analysis of up to several dozen samples.</p> <p>Results</p> <p>Here we introduce pair-barcode sequencing (PBS), an economic and flexible barcoding technique that permits parallel analysis of large-scale multiplexed samples. In two pilot runs using SOLiD sequencer (Applied Biosystems Inc.), 32 independent pair-barcoded miRNA libraries were simultaneously discovered by the combination of 4 unique forward barcodes and 8 unique reverse barcodes. Over 174,000,000 reads were generated and about 64% of them are assigned to both of the barcodes. After mapping all reads to pre-miRNAs in miRBase, different miRNA expression patterns are captured from the two clinical groups. The strong correlation using different barcode pairs and the high consistency of miRNA expression in two independent runs demonstrates that PBS approach is valid.</p> <p>Conclusions</p> <p>By employing PBS approach in NGS, large-scale multiplexed pooled samples could be practically analyzed in parallel so that high-throughput sequencing economically meets the requirements of samples which are low sequencing throughput demand.</p
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On the origin of vertebrate body plan: Insights from the endoderm using the hourglass model
The vertebrate body plan is thought to be derived during the early Cambrian from a worm-like chordate ancestor. While all three germ layers were clearly involved in this innovation, the role of the endoderm remains elusive. According to the hourglass model, the optimal window for investigating the evolution of vertebrate endoderm-derived structures during cephalochordate development is from the Spemann\u27s organizer stage to the opening of the mouth (Stages 1–7, described herein). Regulatory gene expression, examined during these stages, illustrate that the cephalochordate endoderm is patterned into 12 organ primordia. Early vertebrates inherited at least a portion of 6 of these primordia, while the remainder were lost. Of those that were preserved, we demonstrate that the vertebrate symmetric mouth was built on a vestige of the anterior pre-oral pit, that the pre-existing pharyngeal pouch in this chordate ancestor laid the foundation for the new neural crest cell (NCC)-derived vertebrate-type pharyngeal arches, that the thyroid evolved from the posterior endostyle primordim, that the pancreas was derived from the Pdx1-expressing diverticulum primordium, and the small and large intestines originated with the Cdx1-expressing hindgut rudiments. This investigation uncovers the evolutionary foundations of vertebrate endoderm-derived structures, and demonstrates that the number of organ primordia were reduced during evolution
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