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|                           BIOINFORMATICS PROJECTS                            |
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[0] Index (Listed in chronological order.)
________________________________________________________________________________

- Megatron                                                                  [1]
- Simdigree			     					    [2]
- NovaSplice			 					    [3]
- Fit-Hi-C								    [4]
- HiCKRy								    [5]
- HotWASp								    [6]
- TErex								 	    [7]


[1] Megatron
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Megatron is a tool to compute distances between clones in lineage tracing
datasets. The project is a collaboration between the Pinello lab and the 
Morris lab.

Presented at CZI Seed Networks 2020 (cziposter_2020.pdf).

When published, Github: pinellolab/MEGATRON


[2] Simdigree
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Simdigree is a tool to simulate pedigrees using the VCF files from runs of 
SLiM, a forward time population genetics simulator. The purpose of the project 
is to attempt to quantify the probability a given rare or undiagnosed disease 
is monogenic, oligogenic, or polygenic given family history. 

Github: aryakaul/simdigree


[3] NovaSplice
_________________________________________________________________________________

NovaSplice is a command line tool to predict novel splicing events due to 
single nucleotide polymorphisms within non-coding regions. The tool uses 3 
inputs, (1) a reference genome, (2) a genome annotation file (GTF) and (3) a 
VCF file. NovaSplice then loops through every variant in the VCF file and 
computes the score of the canonical splice sites closest to that variant using 
maxentscan. Next, NovaSplice computes the score of every splice site containing
that variant and compares it to the canonical splice site's score. If the novel
splice site's score is close to or greater than the canonical splice site, it 
is reported. Finally, NovaSplice outputs an ordered list of predicted novel 
splice sites. All code is available on Github, and an executable command line 
version of the tool may be installed through bioconda.

Github: aryakaul/novasplice


[4] Fit-Hi-C
_________________________________________________________________________________

Fit-Hi-C is a Python tool to generate statistical confidence estimates for Hi-C
data to reveal significant chromatin contacts. Fit-Hi-C works by fitting a 
monotonically decreasing spline to the probability of a contact given the 
genomic distance of the interaction genetic loci. The null hypothesis may then 
be refined by refitting a new spline after discarding outliers. Fit-Hi-C was 
first developed by Dr. Ferhat Ay, and I've been working on optimizing the code 
for speed and memory usage. Fit-Hi-C is freely available and easily installable
through the Python Package Index, Github, or Bioconda.

Github: ay-lab/fithic


[5] HiCKRy
_________________________________________________________________________________

HiCKRy is a Python3 tool to normalize Hi-C contact maps using the Knight-Ruiz 
algorithm for matrix balancing. The normalization is done under an assumption 
of equal visibility i.e. if there were no bias then each locus in the genome 
would give rise to an equal number of reads in a Hi-C experiment. This
normalization allows the user to correct for all factors that may cause bias 
without explicitly modeling them. The KR algorithm has been demonstrated to be
significantly faster than the iterative method employed by Imakaev et al., and
has been shown to be scalable to high resolution Hi-C datasets. 

Github: ay-lab/HiCKRy


[6] HotWASp
_________________________________________________________________________________

Genome wide association studies (GWAS) represent a powerful tool to interrogate 
the relationship between genetic variation and diseases. Unfortunately, 
conventional GWAS studies suffer from an extremely high statistical burden due
to the extreme number of hypotheses being tested. To help boost the statistical
power of GWAS, HotWASp was developed. HotWASp works by running heat propagation
on a given gene-gene network with initial heats inversely correlated with the 
p-values found through GWAS. HotWASp was developed as part of the final project
for BNFO 286, a UCSD course on Network Biology and Biomedicine taught by Dr. 
Trey Ideker.

Github: aryakaul/HotWASp


[7] TErex
_________________________________________________________________________________

Transposable elements (TEs) are unique DNA sequences capable of autonomously 
'moving' around the genome. Found abundantly throughout eukaryotic and
prokaryotic genomes, TEs are capable of driving genomic evolution and gene 
regulation by enabling translocation, causing exon shuffling, and increasing 
genetic diversity. TErex presents a simple tool to easily identify all 
transposable elements given an input fasta sequence file using the Dfam 
database. TErex was developed as part of the final project for CSE 180, 
Biology meets Computing.

Github: aryakaul/TErex1.0




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Arya Kaul (C) now - forever