# MOMIC: A Multi-Omics Pipeline for Data Analysis, Integration and Interpretation
## 2 Materials and Methods
### Implemented Protocols  
We describe below the protocols developed. These are executed on publicly available datasets and synthetic data related to Alzheimer Disease and serves as an illustrative example of how to use the pipeline to carry out customised analysis. Figures S1-S4 from the Supplementary Material precisely describe the collection of notebooks designed for each protocol, defining the individual tasks, the flow of information and expected inputs and outputs. The user manual and the jupyter notebooks contain more detailed explanations and documented source code. Genome Wide Expression Studies (GWES) from microarrays. RNA microarrays can simultaneously measure the expression level of thousands of genes within a particular mRNA sample [9]. This can be used to compare the level of gene transcription in clinical or biological conditions in order to find differences in expression levels between predefined groups of samples. This pipeline starts from raw expression data and ends with a set of differentially expressed genes, as shown in Figure 2. In order to capture these significant genes, the standard protocol to follow comprises data pre-processing, differential expression analysis (DE) and annotation steps. Pre-processing involves transforming raw data into a matrix of normalized intensities, using the RMA [10], limma [11] or preprocessCore [12] R packages depending on the data platform. Batch effect removal is also available through the combat function of the sva package [13]. DE is performed with limma, an R package based on linear models for microarray data, including optional correction for covariates, followed by annotation to translate probes to genes, using Entrez and Human Genome Nomenclature Committee (HGNC) identifiers. Additionally, heatmaps, PCA and volcano plots can be easily generated to assist the interpretation of results. Genome Wide Expression Studies (GWES) from RNASeq. RNA-Seq is a particular technology-based sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment. This pipeline starts from raw sequence reads, and ends with a set of differentially expressed genes (see Figure 3). The steps to complete this analysis are: quality check of the raw reads using fastqc [14], alignment of reads against the reference genome using STAR [15], quality control of aligned reads (by inspection of QC plots) and reads quantification with STAR. Differential expression can be then performed using DESeq2 [16], an R package based on a model using the negative binomial distribution, followed by annotation with biomaRt library [17]. As for microarrays, plots like MA, heatmap and PCA can be easily obtained. Figure 2: Microarray analysis workflow. Genome Wide Association Studies (GWAS): GWAS is an observational study of a genome-wide set of genetic variants in different individuals to examine if any variant is associated with a trait. The GWAS protocol is available for genome builds GRCh37/hg19 and GRCh38/hg38, including the UCSC liftOver tools for updating genetic positions. This pipeline starts from genomic data in PLINK format, and ends with Single Nucleotide Polymorphisms (SNPs) annotated for surrounding genes and rs identifiers (dbSNP v150 for the GRCh37 pipeline and dbSNP v151 for the GRCh38 pipeline), which are then aggregated to the level of whole genes, testing the joint association of all markers in the gene with the phenotype, as depicted in Figure 4. Procedure followed here is a composition from [PERSON] et al. protocol [18] and [PERSON] et al. [19]. Steps are: initial quality control excluding bad quality individuals and SNPs, genotype imputation using the Michigan [20] or TopMed [21] imputation servers, case control association study using PLINK [22] and gene-wise statistics computed using MAGMA [23]. SNP level data can be visualised with Manhattan and QQ plots [24]. Proteomics: Differential expression between samples can be also explored in MOMIC at the protein level.