Gene Prediction

We reported previously that human geneMAGE-1 directs the expression of a tumor antigen recognized on a melanoma by autologous cytolytic T lymphocytes. Probing cosmid libraries with aMAGE-1 sequence, we identified 11 closely related genes. The analysis of hamster-human somatic cell hybrids indicated that the 12MAGE genes are located in the q terminal region of chromosome X. LikeMAGE-1, the 11 additionalMAGE genes have their entire coding sequence located in the last exon, which shows 64%-85% identity with that ofMAGE-1. The coding sequences of theMAGE genes predict the same main structural features for allMAGE proteins. In contrast, the promoters and first exons of the12 MAGE genes show considerable variability, suggesting that the existence of this gene family enables the same function to be expressed under different transcriptional controls. The expression of eachMAGE gene was evaluated by reverse transcription and polymerase chain reaction amplification. Six genes of theMAGE family includingMAGE-1 were found to be expressed at a high level in a number of tumors of various histological types. None was expressed in a large panel of healthy tissues, with the exception of testis and placenta.

Sugarcane is a major feedstock used for biofuel production worldwide. Sugarcane cultivars (Saccharum spp) are derived from interspecific hybridization obtained a century ago by crossing Saccharum officinarum (2n=8x=80) and S. spontaneum (2n=5x=40 to 2n=16x=128). The challenge in a sugarcane genome sequencing project is the size (10 Gb) and complexity of its genome structure that is highly polyploid and aneuploid (2n=

The potential for use of the cytochrome-pathway electron-transfer inhibitors antimycin A and myxothiazol in the selection of plant mitochondrial genome transformants was investigated. The net growth of Nicotiana tabacum L. (tobacco) suspension-culture cells was reduced by these inhibitors, but complete repression of cell growth occurred only in the presence of both cytochrome and alternative electron-transfer-pathway inhibitors. Antimycin A and myxothiazol bind to and block electron transfer through different sites in the cytochrome b (COB) subunit of the mitochondrial bc1 respiratory complex (complex III). The nucleotide sequence of the tobacco cob gene was determined and found to predict highly conserved glycine and phenylalanine residues that are associated with sensitivity to antimycin A and myxothiazol, respectively. These residues are altered by mutations that confer resistance to antimycin A or myxothiazol in diverse organisms. Tobacco cob cDNA clones were constructed and sequenced, revealing eight full and 11 partial RNA-editing sites. RNA editing did not, however, alter codons for the conserved glycine and phenylalanine residues associated with sensitivity to the respiratory inhibitors. Antimycin A or myxothiazol, in conjunction with a modified cob gene, may therefore be useful in the selection of tobacco cells carrying a genetically transformed mitochondrial genome.

Objective: The Arabidopsis thaliana Niederzenz-1 genome sequence was recently published with an ab initio gene prediction. In depth analysis of the predicted gene set revealed some errors involving genes with non-canonical splice sites in their introns. Since non-canonical splice sites are difficult to predict ab initio, we checked for options to improve the annotation by transferring annotation information from the recently released Columbia-0 reference genome sequence annotation Araport11. Results: Incorporation of hints generated from Araport11 enabled the precise prediction of non-canonical splice sites. Manual inspection of RNA-Seq read mapping and RT-PCR were applied to validate the structural annotations of non-canonical splice sites. Predictions of untranslated regions were also updated by harnessing the potential of Araport11's information, which was generated by using high coverage RNA-Seq data. The improved gene set of the Nd-1 genome assembly (GeneSet_Nd-1_v1.1) was evaluated via comparison to the initial gene prediction (Gen-eSet_Nd-1_v1.0) as well as against Araport11 for the Col-0 reference genome sequence. GeneSet_Nd-1_v1.1 contains previously missed non-canonical splice sites in 1256 genes. Reciprocal best hits for 24,527 (89.4%) of all nuclear Col-0 genes against the GeneSet_Nd-1_v1.1 indicate a high gene prediction quality.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

Familial hypobetalipoproteinemia (FHBL) and abetalipoproteinemia (ABL) are inherited disorders of apolipoprotein B (apo B)-containing lipoproteins that result from mutations in apo B and microsomal triglyceride transfer protein (MTP) genes, respectively. Here we report three patients with severe deficiency of plasma low-density lipoprotein (LDL) and apo B. Two of them (probands F.A. and P.E.) had clinical and biochemical phenotype consistent with ABL. Proband F.A. was homozygous for a minute deletion/insertion (c.1228delCCCinsT) in exon 9 of MTP gene predicted to cause a truncated MTP protein of 412 amino acids. Proband P. E. was heterozygous for a mutation in intron 9 (IVS9-1G>A), previously reported in an ABL patient. We failed to find the second pathogenic mutation in MTP gene of this patient. No mutations were found in apo B gene.The third proband (D.F.) had a less severe lipoprotein phenotype which was similar to that of heterozygous FHBL and appeared to be inherited as a co-dominant trait. However, he had no mutations in apo B gene. He was found to be a compound heterozygote for two missense mutations (D384A and G661A), involving highly conserved regions of MTP. Since this proband was also homozygous for ɛ2 allele of apolipoprotein E (apo E), it is likely that his hypobetalipoproteinemia derives from a combined effect of a mild MTP deficiency and homozygosity for apo E2 isoform.

Fungal genome annotation is the starting point for analysis of genome content. This generally involves the application of diverse methods to identify features on a genome assembly such as protein-coding and non-coding genes, repeats and transposable elements, and pseudogenes. Here, we describe tools and methods leveraged for eukaryotic genome annotation with a focus on the annotation of fungal nuclear and