# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Ribosome Profiling Data Extraction
#### 3.1.1 Preprocessing of Ribosome Profiling Data  
To illustrate the statistical procedure employed in this work, we analyse a set of _E. coli_ Ribo-seq profiles. For this purpose, the data stored in the Gene Expression Omnibus [28] repository were used. Specifically, our analysis concerns a systematic comparison of Ribo-seq profiles, each belonging to a different GEO series (A _series_ is a collection of datasets that include at least one group of data--_sample_--from Ribo-seq experiments performed on _E. coli_ in various conditions according to the most used experimental protocol.), referring to experiments performed culturing wild-type E. coli strains under control conditions. In particular, our analysis regarded a subset of eight samples (labelled from Dataset 1 to Dataset 8) obtained through experiments characterised by K-12 MG1655 genotype and cultured in a MOPS-based medium. Table 1 reports the GEO Series ID and GEO sample ID of raw Ribo-seq dataset used in this experiment.  
To reconstruct the Ribo-seq profiles starting from raw Ribo-seq data, represented by the FASTA format (The FASTA format is a text-based format for representing nucleotide sequences in which base pairs are indicated using single-letter codes [A,C,G,T] where A = Adenosine, C = Cytosine, G = Guanine, T = Thymidine), the reads are mapped against  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset ID** & **GEO Series ID** & **GEO Sample ID** & **Ref** \\ \hline Dataset 1 & GSE64488 & GSM1572266 & [29] \\ Dataset 2 & GSE90056 & GSM2396722 & [30] \\ Dataset 3 & GSE2899 & GSM1874188 & [31] \\ Dataset 4 & GSE53767 & GSM1300279 & [32] \\ Dataset 5 & GSE51052 & GSM1399615 & [33] \\ Dataset 6 & GSE77617 & GSM2055244 & [34] \\ Dataset 7 & GSE35641 & GSM872393 & [35] \\ Dataset 8 & GSE88725 & GSM2344796 & [36] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The samples chosen for our analysis belong to different GEO series. Column 1: Dataset ID chosen to refer to the eight samples in this work; Column 2: GEO Series ID; Column 3: GEO Sample ID; Column 4: references. The listed ID can be used as access keys to the GEO Database ([[https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)]([https://www.ncbi.nlm.nih.gov/geo/](https://www.ncbi.nlm.nih.gov/geo/)), accessed on 29 June 2022) to find a detailed description of both specific series and samples.  
the whole set of coding sequences in _E. coli_, taken from the EnsemblBacteria database [37]. Then, we extracted and counted the number of reads mapping to each gene from the SAM alignment file (The Sequence Alignment Map (SAM) is a text-based format originally used for storing biological sequences aligned to a reference sequence.) using BEDTools [38]. The genomic coordinates are stored in a BED file (The BED file is a tab-delimited text file used to store genomic regions where each feature is described by chromosome, start, end, name, score, and strand.) to build the Ribo-seq profiles representing the input of the subsequent analysis. Each ORF can be associated to a specific Ribo-seq profile, a histogram that counts the number of reads that cover each nucleotide position. To realize the pairwise comparison of ORF-specific Ribo-seq profiles coming from independent datasets, we decided to proceed as described in the following.