The Five Obstacles You Will Face in Your First Year of Grad School

Officially finished my first year at UC Davis as a Biochemistry, Molecular, Cellular and Developmental Biology (BMCDB) graduate student. When I think back to the start of my first year I realize I was very anxious and confused. I had heard so many horror stories about graduate school and was really not sure what to expect. Hopefully this will help with advice on the graduate school environment.

 1) Finding the one (your advisor):

http://www.phdcomics.com/comics/archive.php?comicid=1296

This one is stressful and tough, after all you will be working with this person for 4+ years. Even before deciding on a school make sure there are lots of options.  Many of my top rotation choices did not have funding or had just left the school or the lab just wasn't what I thought it would be. Also make sure to contact older graduate students in your program and get advice on different labs and what the work ethic is like. In hindsight rotating in the summer really made a huge difference. I not only gained an extra rotation but also could easily meet with professors before the chaos began. It is best when leaving a lab to let them know you are weighting your options but really enjoyed your time there. After rotations have finished you should discuss potential thesis projects with each lab before making your decision.  Just remember choose your rotations wisely, start early, and work hard :)

2) Pass your classes:

It is actually easier then it seems to pass as long as you complete all the assignments and actually try. This does not mean that the workload is not heavy! Juggling classes, assignments and rotations is enough to make you want to nap 24/7. It's all about focusing your energy in the right place. Keep in mind that grades do not matter, while where you will be spending your Ph.D does! In other words do not stress grades too much. Form study groups, try hard and you will be fine. Part of grad school is the realizing that there are a lot of subjects that you know very little about where others in your cohort maybe experts.  Within study groups show up with your work done, so you can not only defend you answer but admit to your mistakes and learn from others.

3) Get funding:

Start looking, writing and applying early, as in the summer before you start. I was forced to start my NSF application the summer before and was very grateful that I had. Once school starts there are so many other things to worry about. Also apply for everything even if you don't feel competitive enough. No matter how secure your funding is, many of us learned throughout rotations just how rare a professor with funding is. The best funding advice I could give is to double/triple check that all letter of rec writers will write outstanding letters and have proposals read by multiple people from varying departments and sell yourself!

4) Make Friends:

http://whatshouldwecallgradschool.tumblr.com/post/55377281333/dancing-at-grad-student-parties


Last but not least you should be making friends in graduate school, these are your soon to be collegues and possibly people you will encounter in your future. These are people you will see everyday, who encounter the same struggles, be friends not enemies. The friendship part was very unexpected for me yet helped tremendously. You are going to be stressed and confused and have worries. While it's nice at the end to celebrate your hard work with these people they are also great support throughout. These people are smart and will be able to help you throughout your graduate career. After all you all were accepted into the same program and one thing I realized over time was that everyone in our cohort has their own thing they excel at at. Also it's amazing to finally have friends just as nerdy as you! Shout out to @annaphase



HAVE FUN!

RNASeq Analysis: The Basics

Strange and Mysterious File Types (you might encounter)

Sequence Read Archive format (SRA): This is an NCBI specific file format used because of its ability to compress read sequence information. This is often the output of many illumina sequencing pipelines.

Fastq file: SRA files can be converted to Fastq files, these are similar to Fatsta files and contain a header, associated genomic sequence and a quality score for the sequence. This is often encoded in binary and needs to be read by quality control algorithms. Thus, Fastq files contain your raw sequence information.

BAM and SAM Files: These are your alignment files where SAM stands for sequence alignment file and BAM is the binary form of this. While BAM is unreadable by humans it is often used because it is more memory efficient and quicker for computer algorithms to read in. These are obtained by often by Bowtie or Tophat after your Fastq reads have been aligned to your reference genome

GTF/GFF: These are often used as reference for counting how many reads map to genomic regions. These are tab delimited files containing the , start, stop, chromosome, and strand information along with name of a genomic region (such as gene, transposon, mRNA).

RNASeq process in flow chart on top. Bottom indicated different file formats and types of analysis for each step.

Pre-processing:

If files are obtained from  NCBI’s Gene  Expression Omnibus (geo) then they are most likely in .SRA format and need to be converted to fastq using the fastaq-dump package.

Quality control:

Before processing the data reads must first be trimmed for adapter sequences and reads of bad quality should be filtered out. A great tool for easily viewing the distribution of  quality scores is FASTQC. I use trimreads to both filter low quality reads and trim adaptor sequences. For more detailed analysis others have used many components of the fastx toolkit.

Alignment to reference genome:

Your fastq file contains your RNA small sequence reads. These need to be aligned back to a reference genome. Reference genome: Model organism you are using, sequence containing coding sequence only, microRNA only or entire genome. BOWTIE excels both in speed and memory efficiency for aligning short sequence reads back to a long reference sequence. In order to obtain this efficiency the reference genome must first be indexed. This creates a database of keys (in this case they would be cds sequences) for each record and positions them into a memory efficient tree. An index is created simply by:

bowtie-build reference_genome name_for_refrence_genome

TopHat/Bowtie can then be used to align the fastq sequences to the index reads. TopHat is often preferred in RNAseq analysis because while it is build on top of Bowtie it is also able to identify splice junctions between exons.

Tophat --bowtie1 name_for_refrence_genome fastq_reads

Determining differentially expressed gene:

Once files have been aligned to a reference genome count data can be obtained.

Count Data: These are counts of RNASeq reads that align to a gene (or other genomic regions indicated by the GTF file). Therefore this tab-delimited file contains gene_ids and their corresponding RNA counts from your data.

To analyze for differential expression two programs are commonly used DeSeq or EdgeR. You can read up on comparisons of these two here:

• A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.
• Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing
• DESeq vs edgeR Comparison

Both programs assume that the number of reads in the sample can be modeled using a negative bionomial distribution.

Additional Resources:

Amazing Webpage for RNASeq help

My Github page

5 Apps All Graduate Students Should Have

1) Wunderlist

The one thing I have repeatedly learned in graduate school is that you will always be adding to your TODO list. This list is never ending and often bogs me down. Wunderlist works well for me because it allows me to set deadlines and has a friendly interface. I keep everything from school work to twitter post on it =)

2) Google Scholar

If you have published a paper you should make a Google Scholar account. If you haven't published yet you should make a Google Scholar too! Google Scholar is like a Facebook for scientist, it allows others to easily find papers published by you and who you have collaborated with. It also recommends papers for you based on papers you have read and what you have published.

3) nvALT

You are not always going to have time to take notes, sometimes you just want to jot something down quickly. This app allows users to take quick notes and find them easily in the future. Also it allows you to write (and preview) your notes in HTML and Markdown so that information can be easily be transferred to a website or maybe even a blog.

4) Dropbox/Google Drive

If you have not started using Dropbox or Google Docs yet, get on it! This allows you to access your information from any computer anywhere any time. That research paper you were putting off reading, put off no more. Another bonus to these services is that information is stored in the cloud, free backup. Although you do not ever think your computer will crash and 4 hours of work will be destroyed, it will happen, TRUST ME!

5) StayFocused Chrome Extension 

Also available for firefox and safari browsers.  This app allows you to spend a limited amount of time on distracting sites (such as Facebook) before telling you to get back to work and blocking you from that site for the rest of the night.