A technical guide to identifying miRNA normalizers using TaqMan Advanced miRNA Assays

| August 26, 2016

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MicroRNAs (miRNAs) represent a class of regulatory biomolecules with roles in diverse processes such as cell proliferation, differentiation, apoptosis, and oncogenesis [1]. In recent years, technological advances in research tools including qPCR, microarrays, and nextgeneration sequencing (NGS) have enabled sensitive detection of miRNAs. However, accurate quantifi cation of miRNAs using qPCR is largely dependent on proper normalization techniques, the absence of which can lead to misinterpretation of data and incorrect conclusions [1]. The goal of most miRNA experiments using qPCR is to identify differences in expression between two groups of samples, typically a normal (control) and a mutated (test) sample group. The purpose of normalization is to remove any differences between these two groups other than that which is a true representation of expression levels of the miRNAs in the mutated state.

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Click4Tag

CLICK4TAG SAS, créée en décembre 2014, développe et commercialise des solutions innovantes permettant de détecter/dénombrer/concentrer rapidement des microorganismes pathogènes. Elle proposera d'ici moins de deux ans des kits d'identification ou de dénombrement rapide de Legionella pneumophila, bactérie responsable de la Légionellose.

OTHER ARTICLES

Biotech: Finding The DNA For Success

Article | April 3, 2020

The integration of artificial intelligence within life sciences is making drug discovery and development more innovative, less labor intensive and more cost-effective, says Deloitte’s annual global outlook. According to Deloitte’s 2020 Global Life Sciences Outlook, the biotech sector is at an inflection point. To prepare for the future and remain relevant in the ever-evolving business landscape, biopharma and medtech organizations will be looking for new ways to create value and new metrics to make sense of today’s wealth of data, the report overview says. As data-driven technologies provide biopharma and medtech organizations with treasure troves of information, and automation takes over some mundane tasks, new talent models are emerging based on purpose and meaning. The integration of artificial intelligence (AI) and machine learning approaches within life sciences is making drug discovery and development more innovative, time-effective and cost-effective, the Deloitte report states.

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Ruminating on Bioprocessing 4.0

Article | April 3, 2020

The Bioprocessing 4.0 concept seeks to apply automation and technology to the digital transformation of biologics manufacturing. As the paradigm moves forward, it faces barriers to its adoption, according to Eric Langer, president of BioPlan Associates. “Perhaps the greatest challenges involve unsecured links and adapting the applications to areas where automation is critically needed today,” says Langer. “Unresolved security issues could seriously affect a company’s data in a regulated environment, so they will need to have iron-clad anti-hacking protection in place. Unfortunately, cyber security is not yet a top focus for the industry.”

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DIAGNOSTICS

Making Predictions by Digitizing Bioprocessing

Article | April 3, 2020

With advances in data analytics and machine learning, the move from descriptive and diagnostic analytics to predictive and prescriptive analytics and controls—allowing us to better forecast and understand what will happen and thus optimize process outcomes—is not only feasible but inevitable, according to Bonnie Shum, principal engineer, pharma technical innovation, technology & manufacturing sciences and technology at Genentech. “Well-trained artificial intelligence systems can help drive better decision making and how data is analyzed from drug discovery to process development and to manufacturing processes,” she says. Those advances, though, only really matter when they improve the lives of patients. That’s exactly what Shum expects. “The convergence of digital transformation and operational/processing changes will be critical for the facilities of the future and meeting the needs of our patients,” she continues. “Digital solutions may one day provide fully automated bioprocessing, eliminating manual intervention and enabling us to anticipate potential process deviations to prevent process failures, leading to real-time release and thus faster access for patients.” To turn Bioprocessing 4.0 into a production line for precision healthcare, real-time release and quickly manufacturing personalized medicines will be critical. Adding digitization and advanced analytics wherever possible will drive those improvements. In fact, many of these improvements, especially moving from descriptive to predictive bioprocessing, depend on more digitization.

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Closing bacterial genomes from the human gut microbiome using long-read sequencing

Article | April 3, 2020

In our lab, we focus on the impact of the gut microbiome on human health and disease. To evaluate this relationship, it’s important to understand the particular functions that different bacteria have. As bacteria are able to exchange, duplicate, and rearrange their genes in ways that directly affect their phenotypes, complete bacterial genomes assembled directly from human samples are essential to understand the strain variation and potential functions of the bacteria we host. Advances in the microbiome space have allowed for the de novo assembly of microbial genomes directly from metagenomes via short-read sequencing, assembly of reads into contigs, and binning of contigs into putative genome drafts. This is advantageous because it allows us to discover microbes without culturing them, directly from human samples and without reference databases. In the past year, there have been a number of tour de force efforts to broadly characterize the human gut microbiota through the creation of such metagenome-assembled genomes (MAGs)[1–4]. These works have produced hundreds of thousands of microbial genomes that vastly increase our understanding of the human gut. However, challenges in the assembly of short reads has limited our ability to correctly assemble repeated genomic elements and place them into genomic context. Thus, existing MAGs are often fragmented and do not include mobile genetic elements, 16S rRNA sequences, and other elements that are repeated or have high identity within and across bacterial genomes.

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Spotlight

Click4Tag

CLICK4TAG SAS, créée en décembre 2014, développe et commercialise des solutions innovantes permettant de détecter/dénombrer/concentrer rapidement des microorganismes pathogènes. Elle proposera d'ici moins de deux ans des kits d'identification ou de dénombrement rapide de Legionella pneumophila, bactérie responsable de la Légionellose.

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