RNA sequencing (RNA-Seq) is a powerful technique to study the transcriptome of an organism at a given moment. As its name suggests, RNA-Seq is sequencing the RNA molecules from the sample. But how are the samples prepared? Here I will present a summary of this process:
Disclaimer: This post is not a guide or protocol to perform RNA extraction for RNA-Seq. The objective giving an overview of the process, highlighting the most important steps.
Continue readingLast week I attended the COXIC seminar (joint seminar Oxford – Imperial focused on networks and complex systems) organised by Florian Klimm from Imperial College London (and former OPIG member!). We had several interesting at the seminar. However, one of them caught my eye more than the rest. It was the talk of Dr Sanjukta Krishnagopal (UCL) titled Predicting Parkinson’s Sub-types through Trajectory Clustering in Bipartite Networks, of which I will give a quick insight. Hope you like it (at least) as much as I did!
This blogpost is based on these two articles:
Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information.
In my latest paper, we introduce the concept of signed distance correlation as a measure of dependency between two variables and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson or Spearman correlations.
We are starting the LGBT Pride Month, which commemorates the Stonewall Riots (1959). It has rained a lot since that June 51 years ago when a group of transgender, gay, lesbian, and bisexual people rebelled against the police fighting for their rights, inexistent at that time. Fortunately, the situation has changed for the better: in 2011 the UN National Assembly approved the first Human rights, sexual orientation and gender identity resolution, and the difference between sex and gender is not and up to date, 29 different countries recognise same-sex marriage. Therefore, do we still need to celebrate/commemorate/revindicate LGBT Pride? Yes, yes and one thousand times yes. Why? Here I give you only 10 reasons, but it would not be difficult finding 100 more.
Continue readingIf you are reading this blog, I am sure you will agree that science and research are essential, and even more in the context of a pandemic. Concepts such as PCR, antibody, and herd immunity are slowly getting into people’s vocabulary. This fact makes me quite happy but puts in evidence the lack of scientific knowledge among the population.
Continue readingBeyond the thousands of deaths and the millions of infected people, the COVID-19 pandemic has quickly transformed Western society. This virus has provoked the confinement of millions of people in their houses, the closure of bars, restaurants, and pubs, schools, museums, and theatres. However, for this post, I will focus on another side effect of the pandemic: the spread of canards, which flow even faster than the virus itself.
Continue readingOne of the challenges that biology-related sciences are facing is the exponential increase of data. Nowadays, thanks to all the sequencing techniques which are available, we are generating more data than the amount we can study. We all love all the genomic, epigenomic, transcriptomic, proteomic, … , glycomic, lipidomic, and metagenomic studies because of the rich they are. However, most of the times, the analysis of the results uses only a fraction of all the generated data. For example, it is quite frequent to study the transcriptome of an organism in different environments and then just focus on identifying which 2 or 3 genes are upregulated. This type of analyses do not exploit the data to its maximum extent and here is where network analysis makes its appearance!
Last month, I attended the COSTNET19 Conference in Bilbao (Spain). This conference is organised by COSTNET, a COST Action which aims to foster international European collaboration on the emerging field of statistics of network data science. COSTNET facilitates interaction and collaboration between diverse groups of statistical network modellers, establishing a large and vibrant interconnected and inclusive community of network scientists.
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Background:
Interdisciplinary projects are in fashion. Nowadays, most of the top universities in the world offer “interdisciplinary” doctorate programs. It seems that becoming a specialist in a particular field is not enough to progress in science. Now, students must prove their ability to understand and be proficient in different areas. Why study only Chemistry if you can combine it with Statistics, Programming and Biology? The more tools and concepts you can play with the better.
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