Research and postings on the latest in bioinformatic analysis software that's out there/published and open source. Let's not re-invent the wheel. Disclaimer: I do not promote one software over the next, you'll have to evaluate software on your own.
The National Center for Biotechnology Information (NCBI) provides one of the most extensive sets of web-based tools for biological research. The tools are indispensable when planning genomics experiments, including for qPCR, NGS, and CRISPR. In this presentation, Dr Matt McNeill takes a practical look at getting started with the wealth of NCBI tools, and shares some relevant tips to help you sift through the tools and options that we regularly use. In particular, he focuses on commonly adjusted parameters that will allow you to more effectively use the powerful Basic Local Alignment Algorithm Tool (BLAST) to identify off-target hybridization/annealing events. Dr McNeill also covers practical examples using NCBI tools to design assays.
Rhinoviruses (RV) are increasingly being reported to cause mild to severe infections of respiratory tract in humans. RV are antigenically the most diverse species of the genus Enterovirus and family Picornaviridae .
BMC Genomics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We do not make editorial decisions on the basis of the interest of a study or its likely impact. Studies must be scientifically valid; for research articles this includes a scientifically sound research question, the use of suitable methods and analysis, and following community-agreed standards relevant to the research field.
Specific criteria for other article types can be found in the submission guidelines.
RNA-Sequencing (RNA-Seq) provides valuable information for characterizing the molecular nature of the cells, in particular, identification of differentially expressed transcripts on a genome-wide scale. Unfortunately, cost and limited specimen availability often lead to studies with small sample sizes, and hypothesis testing on differential expression between classes with a small number of samples is generally limited. The problem is especially challenging when only one sample per each class exists. In this case, only a few methods among many that have been developed are applicable for identifying differentially expressed transcripts. Thus, the aim of this study was to develop a method able to accurately test differential expression with a limited number of samples, in particular non-replicated samples. We propose a local-pooled-error method for RNA-Seq data (LPEseq) to account for non-replicated samples in the analysis of differential expression. Our LPEseq method extends the existing LPE method, which was proposed for microarray data, to allow examination of non-replicated RNA-Seq experiments. We demonstrated the validity of the LPEseq method using both real and simulated datasets. By comparing the results obtained using the LPEseq method with those obtained from other methods, we found that the LPEseq method outperformed the others for non-replicated datasets, and showed a similar performance with replicated samples; LPEseq consistently showed high true discovery rate while not increasing the rate of false positives regardless of the number of samples. Our proposed LPEseq method can be effectively used to conduct differential expression analysis as a preliminary design step or for investigation of a rare specimen, for which a limited number of samples is available.
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