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- Stratification of biological samples based on proteomics data.
Abstract: Stratification of biological samples by using high-dimensional data, such as those derived from mass spectrometry-based proteomics approaches, has become a promising strategy to solve biological questions, as well as to classify samples in relation to different phenotypes. In this regard, we have discussed some computational aspects related to the processing of MudPIT data through a class of algorithms wide used in machine learning community, such as Support Vector Machines. Specifically, after a short presentation of the input data structure, we focused on properties and abilities of feature selection and classification models, indicating useful tools for assisting scientists in these computations. Finally, we concluded this review hinting at new strategies of inference which coupled to MS improvement, in instruments and methods, may represent the perspectives of this field.
PubDate: 07/11/2020 04:39:44 am
- The structure of state-of-art gene fusion-finder algorithms
Abstract: Fusion genes, also known as chimeras, play important roles in tumorigenesis and cancer progression. Then, their role becomes crucial in the areas of biomarkers and therapeutic targets investigation. High-throughput sequencing technologies combined with sophisticated bioinformatics tools might facilitate the discovery of such aberrations. A significant number of bioinformatics algorithms have been developed to detect fusion genes. Detection strategies are quite variegated, then we inspect the strategy of 18 fusion finder algorithms to understand how these tools call chimeras.
PubDate: 07/11/2020 04:39:44 am
- Differential expression analysis methods for ribonucleic acid-sequencing
data.
Abstract: High-throughput mRNA sequencing (also known as RNA-Seq) promises to be the technique of choice for studying transcriptome profiles, offering several advantages over old techniques such as microarrays. This technique provides the ability to develop precise methodologies for a variety of RNA-Seq applications including gene expression quantification, novel transcript and exon discovery, differential expression (DE) analysis and splice variant detection. With the introduction of this technique, there has been a significant effort in developing new methods and statistical models to accurately model RNA-Seq data and test for differences in gene expression between biological conditions. In this manuscript, we review some of the most recently and widely used methods for differential expression analysis. We provide a detailed review of these methods by looking at three main aspects: (I) statistical methods for normalization, (II) statistical modeling of gene expression and (III) statistical methods for differential expression testing.
PubDate: 07/11/2020 04:39:44 am
- Inhibitory activity of hemagglutinin and neuraminidase protein casepase
activity in swine flu [H1N1].
Abstract: Influenza A virus (H1N1) currently prevailing in Asia causes fatal pneumonia and multiple organ failure in humans. The two principle polypeptides, the Hemagglutinin (HA) and the Neuraminidase (NA), which are the target for the neutralizing antibodies immune response. Despite intensive research, understanding of the characteristics of influenza A virus that determine its virulence is incomplete. There are various immune cells inactivation is causing swine flu. Therefore, current, hot task of influenza virus research is to look for a way how to get us closer to a universal vaccine. In this study aims to identify the 3D structure of H1N1-M2 channels, HA2 gp and eM2 protein structures of patient samples from swine flu. With an explicit water-membrane environment, the molecular docking studies were performed for Oseltamivir and Zanamivir, these two commercial drugs generally used to treat influenza A virus infection. It was found that their binding affinity to the H1N1-M2 channel is significantly lower than that to the H5N1-HA2 gp, M2 and eM2 protein channel, fully consistent with the recent report that the H1N1 swine virus was resistant to the 2 drugs. The findings the relevant analysis reported here might provide useful structural insights for developing effective drugs against the new swine flu virus.
PubDate: 07/11/2020 04:39:44 am
- How to recognise, resolve and prevent three forms of statistical nonsense
in bioinformatics.
Abstract: Modern biotechnologies have provided biomedical researchers the golden opportunity to quickly and economically collect comprehensive data on the genome, epigenome, transcriptome, and proteome of multiple tissue samples. Simultaneously, a plethora of novel data analysis methods have been developed in an attempt to rapidly translate these massive data sets into biological knowledge. The development of these new technologies and data analysis methodologies has fueled an explosion of biological discoveries. Some of these discoveries have been successfully confirmed by other groups and translated into better therapies. However, some published findings have been debunked as artifacts of shoddy experimental design or nonsensical data analysis. Thus, it is important to recognize, resolve, and prevent these forms of nonsense from corrupting ongoing and future studies. Here, three specific forms of nonsense in bioinformatics are described: experimental designs that confound technical factors with biological factors, preprocessing procedures that computationally define biologically meaningless variables for subsequent statistical analysis, and determining statistical significance with data analysis methods that use computational models which ignore the experimental design and other biological realities. Additionally, strategies to recognize, resolve, and prevent each of these forms of nonsense are described. Finally, related open research problems and opportunities are discussed.
PubDate: 07/11/2020 04:39:44 am
- Life sciences driven customized Linux distributions.
Abstract: The article has been forwarded to the production team. The processing may take few weeks. Then the proof will be forwarded to the corresponding author. The final PDF and HTML files will be uploaded when the corrections to the proof are returned by the corresponding author.
PubDate: 07/11/2020 04:39:44 am
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