Identification And Quantification Of Alternative Translicing In Genome-Wide Transcript Analysis

Paralogs are homologous sequences separated by a duplication event. These genes occupy two different functions in the same genome.

Gene splicing is simply explained as the addition of genes from one organism’s genome to another’s. This branch of science is focused on altering the DNA of organisms in ways such as enhancing plants and animals’ physical characteristics or on repairing the damaged genomes of humans. As with the alteration of anything to do with human life, the use of gene splicing on humans has raised a controversy over its ethics. Some people believe that gene splicing could be beneficial to the medical field while others believe that it will cause many problems with society. Despite its few ethical concerns gene splicing is something that scientists should continue to develop due to its current use in natural gene expression, occurrence in nature, current

The fourth exon of the MECP2 gene is the largest one; it contains a larger than 8.5 kb 3′-untranslated region, with numerous polyadenylation sites that enable the generation of multiple transcripts varying in size. Alternative polyadenylation in the 3′-untranslated region gives rise in a highly expressed 10.1-kb transcript in the foetal brain and a 5-kb transcript in the adult brain (Coy JF.,

Long ago stories, legends, and myths were created describing humans who were infused with the body parts of animal. Creatures such as mermaids, centaurs, and Satyrs were placed into our minds and we could only imagine what it would be like to meet these creatures. They became so numerous that they were given a category, Anthropomorphism. Over time and with our knowledge of science increasing, body parts that may have been considered to be part of Anthropomorphism were explainable. Webbed hands and feet, humans being born with a tail; and even being born with an extra appendage like an extra toe or finger are no longer a disfiguring ailment. But what

[3] T. Tengs et al, "Microarray-based method for detection of unknown genetic modifications," BMC Biotechnology, vol. 7, (1), pp. 91, 2007. Available: http://www.ncbi.nlm.nih.gov/pubmed/18088429. DOI: 10.1186/1472-6750-7-91.

Different approaches have been developed in order to understand the function of these genes. The first methods involved random integration of short hairpin RNAs (shRNAs) or short inhibitory RNAs (siRNAs) to inhibit the production of a protein or overexpress it to gain insights about its function. 1

Like other next-generation sequencing methods, three general steps are required for exome sequencing: library creation, sequencing, and data analysis. The size and quality of tested samples are screened before establishing the library. In library creating, DNA molecules are fragmented into a suitable size and fused with platform-specific adapters. After size selection step free adapters elimination, PCR is performed to select for molecules containing adapters at both ends and to generate sufficient quantities for sequencing2. The sequencing is performed in specific machine such as Illumina Hi-Seq Platform, the working principles were discussed in detail in Topic Three, however, in exome sequencing, only exomes are selected, amplified and

Transposable elements (TEs) such as Arthrobacter luteus (Alu) sequences make up approximately 20% of the human DNA genome. The repeated DNA sequences were once thought to be “junk” for decades after its discovery in the 1940s, however, recent data suggest that these sequences cause codon alteration and splice site relocation. Ultimately these events change the human genome. The transposable elements are believed to have risen from retrotransposition and constant RNA editing over evolution (Kim 2013). The primary mechanism for retrotransposition is caused by adenosine deaminases acting on RNA (ADARs). ADAR reactivity and potency will enable scientists to track small RNA and gene interference activities. The retrotransposition of RNA into common introns serve as indicators of sites of cancer initiation, progression, and therapeutic effects. The influx in the concentration and frequency of production Alu sequences trigger sequence mutation, generating different protein isomers, leading to cancer (Crews 2015).

The Translational Genomics Research Institute (TGen) is a not-for-profit organization specializing in the research of various types of cancer and rare diseases as well as new drug discovery for many of these diseases. It has been a leader in the cancer genomics research field over the last several years. TGen has a number of laboratories, led by individual research scientists, where research is carried out. As a not-for-profit organization, TGen is funded by donations and research grants. By the nature of its work, the culture at TGen has been mostly adhocracy.

Currently, I am involved in developing methodology to identifying differentially expressed genes using single cell RNA sequencing data. scRNA-Seq data has tremendous opportunity to study the expressions of genes at an individual cellular level. However, this type of

Transposable elements are useful for carrying out gene tagging and functional genomics studies. In plants, various transposable element systems like Ac/Ds, En/Spm, and Mu from maize have been utilised for gene determining tools. Of these, Ac/Ds transposons system has been efficiently used for such studies in various heterologous monocot species like rice, barley and dicot species. Wild barley, being a rich source of novel useful genes, can be exploited as a transpososn based gene tagging resource. The aim of this study was to develop new molecular breeding tools for the discovery of novel genes from wild barley. In the first objective, an endogenous Mu transposable element was found in three PGRC wild barley lines and nested inverse PCR (iPCR) technique was utilized to generate flanking sequences from this Mu transposon. Basic bioinformatics analysis of 8 flanking sequences has identified 6 sequences with important genes responsible for certain domesticated traits. In the second objective, wild barley lines Damon and Shechem were crossed with a Ds containing cultivated barley line, TNP11. Four crosses were made with Shechem line and five crosses were made with Damon line and further, two backcrosses were also made with Damon line. These newly developed Ds insertion lines and newly found Mu containing wild barley lines can be utilized for molecular breeding and to carryout functional genomics studies for understanding of useful genes.

Due to the inherent false positives and negatives associated with each tool, combing different tools is expected to remarkably increase the accuracy. In our previous study [6], by integrating outcomes of these 7 tools (SIFT, PolyPhen, PROVEAN, PHD-SNP, PANTHER, SNPs&GO and I-Mutant), a SAAMP algorithm with a pathogenic index (PI) was developed. PI is defined as percentage of ‘damaging’ predictions from these 7 tools (ranging from 0 to 1), where the higher the PI is, the more pathogenic the mutation is. The cut-off value was set at 0.43. When PI was ≥ 0.43 (larger than or equal to 3 ‘damaging’ predictions), the mutation was defined as ‘pathogenic’, otherwise ‘benign’. When tested in the IDUA gene, a sensitivity of 93.8% and a specificity of 80% was achieved, which was better than any individual tool. In this study, by testing each tool in a broad array of genes (Figure 2), we determined the widely varying performances of these tools. Therefore, we decided to optimize the SAAMP algorithm by excluding PANTHER, SNPs&GO and I-Mutant based on their modest performances. SAAMP 2.0 only included PROVEAN, PHD-SNP, SIFT and PolyPhen, and defined the cut-off value of PI as 0.5 (larger than or equal to 2 ‘damaging’ predictions). Notably, there was no specific order for using these 4 bioinformatics tools. Since the performances of the remaining four tools were quite similar, we treated each tool

The protein coding transcripts were compared with sequences in Nr protein and nucleotide (Nt) databases, the Swiss-Prot protein database to assign potential function onto transcripts. The program Blast2GO [7] and InterproScan [8] were used to obtain GO annotation of the transcripts and identify protein domains, respectively. GO and enrichment analyses were performed using GOseq, involving biological process, molecular function, and cellular component. The metabolic pathways were predicted using KAAS [9], and KOBAS 3.0 [10]was used to test the statistical enrichment of differentially expressed lncRNA target genes. Complementarily, genome-wide prediction of the

Characterization of the Lnc11.2 transcript Amplification of the 3’and 5’ends of the Lnc11.2 transcript uncovered the

In this paper, the necessary RNA data set was generated by subcellular fractionation of the 15 cell lines considered to analyse the human transcriptome. Thus isolated RNAs were classified into two categories long (>200 nucleotides) and short (<200 nucleotides) and the long transcripts were further separated into polyadenylated and non-polyadenylated transcripts.