Neurospora_crassa_transcriptomics

Characterizing the gene-environment interaction underlying natural morphological variation in Neurospora crassa conidiophores using high-throughput phenomics and transcriptomics

Quantification:

The quantificaiton scripts are written for the alignment of short read illumina RNA sequencing data. The data was quantified using two standard pipelines. The counts and differentially expressed transcripts were cross validated to ensure the reproducibility of results. The genome annotation was built on GCF_000182925.2_NC12_genomic.fna.gz

CLI:

nohup ./star_quantification.sh &

Analysis:

A simple differential expression analysis was conducted using the quantifications.tab files. The transcript counts were analyzed in R with canaonical transcriptomics softwares from bioconductor.

Genetic model:

Table of MATLAB scripts to calculate varied inheritance models for the discrete complex trait of conidiophore type by the Method of Maximum likelihood using iteratively reweighted least squares. The first 8 columns taken from Table 6 is some of the output of the MATLAB script. The last column is the name of the corresponding MATLAB script. Each script is self contained and can simply be run within MATLAB by hitting run once the script is clicked on and opened.

Model	Χ^2	df	p	X^2 H0 − X^2 HA	rho for HA vs. H0	Alternative HA	MATLAB scripts
Full epistatic	0	0	-	-	-	-	estimator_inh_model_full_espistatic_loglinear_V8.m
αβ=0	13.37	1	<0.0001	13.37 – 0.00 = 13.37	<0.0001	HA = full epistatic	estimator_inh_model_ab_loglinear_V8.m
αβ=α=0	16.27	2	<0.0001	16.27 – 13.37 = 9.12	0.002	HA = αβ=0	estimator_inh_model_ab_a_loglinear_V8.m
βγ=0	43.42	1	<0.0001	43.42 – 0.00 = 43.42	<0.0001	H0 = full epistatic	Estimator_inh_model_bc_loglinear_V8.m
βγ=β=0	43.21	2	<0.0001	43.21 – 43.42	0.64	HA = βγ=0	estimator_inh_model_bc_b_loglinear_V8.m
αβ=β=0	29.88	2	<0.0001	29.88 – 13.37 = 16.51	<0.0001	HA = αβ=0	estimator_inh_model_ab_a_loglinear_V8.m
αγ=0	11.11	1	0.0009	11.11 – 0.00 = 11.11	0.0009	HA = full epistatic	estimator_inh_model_ac_loglinear_V8.m
αγ=γ=0	126.37	2	<0.0001	126.37 – 11.11 = 115.26	<0.0001	HA = αγ=0	estimator_inh_model_ac_c_loglinear_V8.m
αβ=βγ=αγ=0, additive	44.61	3	<0.0001	44.61 – 13.37 = 31.24	<0.0001	HA = αβ=0	estimator_inh_model_additive_fixed_sizes_V8.m
environmental	84.27	6	<0.0001	84.27 – 44.61 = 39.66	<0.0001	HA = additive, H2 = (84.27 – 44.61)/84.27 = 0.47, H0 = environmental model, H1 = full additive model	Estimator_inh_model_environmental_fixed_sizes_V8.m

Heritability:

H0 = environmental model
H1 = full additive model	
H2 = (84.27 – 44.61)/84.27 = 0.47

TO RUN:

$ alias matlab='/Applications/{YOUR MATLAB}/bin/matlab -nodesktop -nosplash $*'

$ matlab

MATLAB:

>>> estimator_inh_model_full_espistatic_loglinear_V8