Bioinformatics in Modern Biology: computational genomics as a tool to unravel soybean genome and gene regulation

# Bioinformatics in Modern Biology: computational genomics as a tool to unravel soybean genome and gene regulation
### Fabricio Almeida-Silva <span class="citation">@almeidasilvaf</span>
### PGBV/UENF
### November 22, 2021

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## Why soybean?

.pull-left[
<br />
.bgb[Food security:] <br />
Human and animal nutritional: oil and protein content.

]

.pull-right[
![](https://images.unsplash.com/photo-1572457224112-06d191bb6d01?ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&ixlib=rb-1.2.1&auto=format&fit=crop&w=816&q=80)

]

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## The soybean genome

52,872 genes (latest assembly).

Strong signatures of 2 WGD (polyploidization) events:

- .bgp[~58 mya:] legume WGD 
  - .bgp[~13 mya:] Glycine-specific WGD
  
Polyploidy provides genomes with the raw material for genetic innovation, especially under rough conditions.

Soybean as a model for evolutionary genomics.
]

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## Soybean functional genomics over the past decade

- genetic diversity
- trait-associated markers (GWAS)
- spatiotemporal dynamics of:
  - gene expression (transcriptomics)
  - protein accumulation (proteomics)
  - metabolites (metabolomics)
  - epigenetic changes (epigenomics)
]

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## Soybean functional genomics over the past decade

- genetic diversity
- trait-associated markers (GWAS)
- spatiotemporal dynamics of:
  - .bgb[gene expression (transcriptomics)]
  - protein accumulation (proteomics)
  - metabolites (metabolomics)
  - epigenetic changes (epigenomics)
]

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## The Soybean Expression Atlas

]

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## The Soybean Expression Atlas

Overall high-quality of samples.

Samples cluster into 3 major groups:

- aerial parts
- underground parts
- seed and seed-related parts

]

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## The Soybean Expression Atlas

Overall high-quality of samples.

Samples cluster into 3 major groups:

- aerial parts
- underground parts
- seed and seed-related parts

Web interface for easy data download and reuse.

Over .n[200] recurring users, who are mainly from the USA and China.

]

---

## The Soybean Expression Atlas

---

# Are we done yet?

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## Exponential accumulation of RNA-seq data

Most groups generating new transcriptome data are in China,
the USA, and Brazil.

New versions of the Soybean Expression Atlas will be released biannually.

Soybean Expression Atlas v.n[2.0]:
- Release in mid-.n[2022]
- Probably .bgp[3x more samples].
]

---

# Exploring the complexity of soybean (*Glycine max*) transcriptional regulation using global gene co-expression networks

---

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## Motivation

Classical transcriptomic studies are limited to particular conditions or case-control setups.

Large-scale coexpression networks can reveal patterns that individual studies cannot, such as:

- sets of co-regulated genes and their regulators

- pathways associated with unique transcriptional profiles

- evolutionary trends, especially among duplicated genes.

]

---

## Aims

<br />

- Unravel biological processes and metabolic pathways associated with each module.

- Identify tissues where each module's expression is enhanced or repressed.

- Predict the regulators of each module's expression.

- Elucidate the fates of duplicated genes at the transcriptional level.

---

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## Module hubs uncover biological processes associated with specific tissues

.footnote[Almeida-Silva *et al.*, 2020. Planta]
.pull-left-1[
<br />
9 modules were enriched in GO terms, pathways and protein domains.

Hubs are enriched in essential genes (i.e. embryonic lethal genes).
]

---

## Major regulators of important biological processes

---

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## Network topology and the possible fate of soybean duplicated genes

.footnote[Almeida-Silva *et al.*, 2020. Planta]
.pull-left[
<br />
Most pairs displayed divergent expression profiles or signs of fractionation.

Greater levels of co-occurrence in modules for WGD-derived pairs (especially from the 13 WGD).
  
  - Increased retention of WGD duplicates involved in intricate systems.
  
Frequency of co-occurrence in modules is higher than the expected by chance.

- transcriptional similarity of part of the duplicates under selective pressures.
  
]

---

## Further reading

.footnote[Almeida-Silva *et al.*, 2020. Planta]
<br /> 
<br />
<img src="figs/sbv/paper_planta.png" width="95%" style="display: block; margin: auto;" />

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# Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

---

## Motivation

GWAS can identify .bgb[causative SNPs] associated with traits, but not .bgb[causative genes].

Current methods lead to high false-positive and false-negative rates.
]

<br />

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## The rationale: guilt-by-association

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---

## Aim

<br />
.center[.font120[Identify high-confidence candidate genes involved in resistance to fungal diseases by integrating GWAS and coexpression networks]]

----

- Inferring coexpression networks is very hard, especially due to data pre-processing.
- Lack of existing methods to integrate GWAS and coexpression networks as we wanted.

---

## Aim

<br />
.center[.font120[Identify high-confidence candidate genes involved in resistance to fungal diseases by integrating GWAS and coexpression networks]]

----

- .bgp[Inferring coexpression networks is very hard, especially due to data pre-processing.]
- Lack of existing methods to integrate GWAS and coexpression networks as we wanted.

---

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.pull-left-2[
## .bold[.dark-blue[BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction]]
]

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## BioNERO

.pull-left[
<br />
R/Bioconductor package that features:
- expression data preprocessing
- gene coexpression network inference
- gene regulatory network inference
- module detection and network statistics
- functional analyses
- network visualization
- network comparison
]

---

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## BioNERO

---

## BioNERO

<br />
.footnote[Almeida-Silva *et al.*, 2021. Functional and Integrative Genomics]

---

## BioNERO

---

## Further reading

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## Aim

<br />
.center[.font120[Identify high-confidence candidate genes involved in resistance to fungal diseases by integrating GWAS and coexpression networks]]

----

- ✅ &nbsp;Inferring coexpression networks is very hard, especially due to data pre-processing.
- .bgp[Lack of existing methods to integrate GWAS and coexpression networks as we wanted.]

---

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.pull-left-2[
## .bold[cageminer: an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks]
]

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## cageminer

---

## cageminer

---

## Further reading

---

## Aim

<br />
.center[.font120[Identify high-confidence candidate genes involved in resistance to fungal diseases by integrating GWAS and coexpression networks]]

----

- ✅ &nbsp;Inferring coexpression networks is very hard, especially due to data pre-processing.
- ✅ &nbsp;Lack of existing methods to integrate GWAS and coexpression networks as we wanted.

---

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## Data overview

<br />

A species must be represented by:
- transcriptome samples
- GWAS-derived SNPs
]

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## Data overview

.footnote[Source: Crop Protection Network | Chiotta *et al.*, 2016 | Daren Mueller | Elevagro | Agrolink]

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## Prioritized candidate genes

<br />

- *Cadophora gregata:* **11**

- *Fusarium graminearum:* **59**

- *Fusarium virguliforme:* **191**

- *Macrophomina phaseolina:* **8**

- *Phakopsora pachyrhizi:* **3**

Highly .bgp[species-specific] response.

]

.pull-right[
![](https://github.com/almeidasilvaf/GCN_GWAS_fungi/blob/main/figs/venn_diagram_candidates.png?raw=true)
]

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## A network of processes

<br />
.pull-left[
Both well-known and novel candidates.

Most candidates likely involved in .bgp[defense signaling].

Hidden treasure? 8% of the candidates encode proteins of unknown function.

]

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## Promising targets for genetic engineering

<br />
.pull-left[
Candidates were scored and ranked with:

where:

`$$\kappa = 2 \text{ if the gene is a transcription factor}$$`

`$$\kappa = 2 \text{ if the gene is a hub}$$`

`$$\kappa = 3 \text{ if the gene is a hub and a transcription factor}$$`

]

---

## Potential accessions in the USDA germplasm

<br />

**Goal:** .bgb[largest] number of .bgb[resistance SNPs] and .bgr[smallest] number of .bgr[susceptibility SNPs].

<br />

GG = 2

AG = 1

AA = 0
]
]

GG = 0

AG = 1

AA = 2 
]
]

---

## Potential accessions in <br /> the USDA germplasm

<br />
.pull-left[

`$$S_{total} = \sum\limits_{i=1}^nS_i \text{ where }S_i = \{0,1,2\}$$`

- There is still room for alelle pyramiding
- Best accessions can be improved through MAS-based breeding or genetic engineering

]

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## A web app to facilitate data reuse

Users can explore the coexpression network we inferred at https://soyfungigcn.venanciogroup.uenf.br.

---

## Further reading

---

## Conclusion and take-home messages

<br />
- The Soybean Expression Atlas is one the largest plant transcriptome databases.

- Gene networks are great tools to .bgb[surf the transcriptomic data tsunami], with a plethora of possible applications (from evolutionary analyses to gene discovery).

- **BioNERO** makes network inference and analysis fairly easy tasks.

- **cageminer** can (and probably will) be used to mine candidate genes associated with many other important traits.

- As new data are generated, sequencing becomes less useful. Extracting knowledge from existing data is our current challenge.

---

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## A new adventure

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## You can find me at: