## Kindly note that I have moved my website and blog and I will no longer be active here. Please visit my new website.

**BioInformatics** – truly an interdisciplinary subject! One needs the knowledge of three disciplines at the very least. First, Biology to understand a problem and its associated constraints, to frame a hypothesis, and to approve or disapprove the conclusions drawn at the end. Second, Computer Science to translate the problem into *computation-friendly language*, to solve the problem as efficiently and as *correctly* as possible (it’s not feasible to reach at the correct or optimised solution of every problem, so we have to make do with an “approximate” one with, preferably, some “guarantees”), and to develop the corresponding software tools and/or libraries. Last, Statistics to set-up the experiments for testing the hypothesis, to evaluate the results given by those solutions/tools, and to interpret/make ‘sense’ of those results.

## The Problem

As a fledgling computer scientist, I feel like I am taking my maiden flight in the sky of BioInformatics. Studying Computer Science has developed my instinct of breaking down and mapping any physical problem to mathematical domain (aka translation) and has given me wings in the form of mathematical foundations, algorithmic skills, and programming capability (to build the solutions). Even statistics seems like an easy and natural extension (as Computer Science also introduces one to the concepts of Combinatorics and Probability theory). On the other hand, my training in biological concepts is too primitive to soar high. Okay! enough of the “flight” analogy.

## The Solution

To achieve the goal of having a thorough grasp over the whole BioInformatics-pipeline, I will have to overcome the ‘handicap’ of non-bio background as well as diving deeper into the core *bioinfo* concepts. For equipping myself with the basic biological building blocks and to learn the ropes of bioinformaticians, the plan seems simple: trying to find the online courses, tutorials, lectures, videos etc. suitable for building up on my current biological knowledge-base and having an appropriate depth that suffices to enable me to look at a phenomenon through computational lens.

### The Hurdles

After going through dozens of courses in text as well as video format, I found the following stumbling blocks. One, the usual one or two -lectures long oversimplified introduction to Biology, as is given in most of the bioinfo courses, left me craving for more. Two, the courses are either too theoretical (algorithmics and mathematics based) or too practical (giving hands-on exposure to the widely-used tools) to make me visualise the whole picture wherein I could see the internal intricacies of the tools as well as connect the input/output flow of various tools .

### Scaling the Hurdles

In an effort to develop the whole picture in my brain, I have decided to make notes in the form of a series of blog-posts. Why blog-posts? Because someone else like me might find them useful. In contrast to the verbose posts that I have written up until now (in order to help mainly the undergrads), these are going to be laconic: listing the resources, summarising the topics, connecting the threads, and so on. A **disclaimer**: the material, its flow etc. will not be my own but more or less follow the resource from which it would be taken.

Keep watching this space for the first part: **Import Bio.Basics** – Basic biological concepts for a computer scientist and an aspiring bioinformatician.