Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Abstract Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below.

Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Abstract Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below. For development of VR-Omics publicly available data was used. The Visium data from 10XGenomics is available at the 10X Genomics website: https://www.10xgenomics.com/resources/datasets. The 10X Genomics Xenium dataset is available under: https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast. The STOmics database is available at: https://db.cngb.org/stomics. The Vizgen MERFISH data release program can be accessed via: https://vizgen.com/data-release-program/. The Tomo-seq data is available via their publication https://doi.org/10.1016/j.cell.2014.09.038 which also contains the MATLAB code for the 3D data reconstruction. The Visium demo was adapted from Asp et al. and can be accessed via the related publication https://doi.org/10.1016/j.cell.2019.11.025 or at https://data.mendeley.com/datasets/zkzvyprd5z/1. The demo datasets generated for VR-Omics can be found at: https://doi.org/10.26180/22207579.v1 or below for download. The 3D Visium data set of the human developing heart adapted from Asp et al. can be found within the application and can be accessed from the main menu following the Visium, Demo context menu. The complete standalone version of VR-Omics (containing Python AW and Visualiser) can be downloaded at https://ramialison-lab.github.io/pages/vromics.html or at https://doi.org/10.26180/20220312.v1 or below for download. Alternatively, the code is available at GitHub (https://github.com/Ramialison-Lab/VR-Omics). To use the GitHub version an installation of Unity Gaming Engine (version 2021.3.11f1) is required. This version does not include the Python AW. The Python AW can be accessed at: https://doi.org/10.26180/22207903.v1. More information of run VR-Omics via Unity can be found in the full documentation accessible at https://ramialison-lab.github.io/pages/vromics.html.

Sample datasets based on and processed using the VR-Omics AW to be visualised in VR-Omics including:S1R1 from the "MERFISH Mouse Brain Receptor Map" (https://info.vizgen.com/mouse-brain-map) SS200000135TL_D1.tissue.gef from the "Stereopy Demo Data" (http://116.6.21.110:8090/share/dd965cba-7c1f-40b2-a275-0150890e005f)SS15 - Zebrafish Junker et al. Cell, https://www.sciencedirect.com/science/article/pii/S0092867414012264Mouse Brain Section (Coronal)" dataset (https://www.10xgenomics.com/resources/datasets/mouse-brain-section-coronal-1-standard)Replicate 1 from the "Fresh Frozen Mouse Brain Replicates" (https://www.10xgenomics.com/resources/datasets/fresh-frozen-mouse-brain-replicates-1-standard)

Test data to be used with VR-Omics. The data has been already processed using the VR-Omics Automated Workflow.

Stitch3d_C18heart - R Script for the Visualisation of Stitch3D multiple slides for software comparison in VR-Omicsmain_C18-DEG - R script DEG analysis Asp et almain_HCA-DEG - R script DEG analysis Litivunika et alcount_c18heart_RA_RV - count matrix for right ventircle vs right atria comparison

This dataset includes all the data used in analysis of miRNA, metabolite, and VR-based behavior measures in 23 collegiate football athletes. Results using this dataset have been published in iScience.

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes1.

BACKGROUND: Long-distance transportation, a frequent practice in the cattle industry, stresses calves and results in morbidity, mortality and growth suppression, leading to welfare concerns and economic losses. Alkaline mineral water (AMW) is an electrolyte additive containing multiple mineral elements and shows stress-mitigating effects on humans and bovines. RESULTS: Here, we monitored the respiratory health status and growth performance of 60 Simmental calves subjected to 30 hours of road transportation using a clinical scoring system. Within the three days of commingling before the transportation and 30 days after the transportation, calves in the AMW group (n = 30) were supplied with AMW, while calves in the Control group (n = 29) were not. On three specific days, namely the day before transportation (day -3), the 30th day (day 30) and the 60th day (day 60) after transportation, sets of venous blood, serum and nasopharyngeal swab samples were collected from ten calves for routine blood testing, serology detection, whole blood transcriptomic sequencing, serum untargeted metabolic sequencing and 16S rDNA sequencing. The field data showed that calves in the AMW group displayed lower rectal temperatures (38.967 vs 39.022 °C; p < 0.001), respiratory scores (0.079 vs 0.144; p < 0.001), appetite scores (0.024 vs 0.055; p < 0.001), ocular and ear scores (0.185 vs 0.338; p < 0.001), nasal discharge scores (0.143 vs 0.241; p < 0.001) and higher body weight gains (30.87 vs 7.552 kg; p < 0.001). The outcomes of laboratory and high throughput sequencing data revealed that the calves in the AMW group demonstrated higher cellular and humoral immunities, antioxidant capacities, intestinal absorption and lipogenesis abilities, and lower inflammatory levels on days -3 and 60. The nasopharynx 16S rDNA microbiome results revealed the different composition and structure of the nasopharyngeal microflora in the two groups of calves on day 30. Joint analysis of multi-omics revealed that on days -3 and 30, bile secretion was a shared pathway enriched by differentially expressed genes and metabolites, and there were strong correlations between the differentially expressed metabolites and the main genera in the nasopharynx. CONCLUSIONS: These results suggest that AMW supplementation enhances peripheral immunity, nutrition absorption and metabolic processes, subsequently affecting the nasopharyngeal microbiota and improving the respiratory health and growth performance of transported calves. This investigation provided a practical approach to mitigate transportation stress and explored its underlying mechanisms, which are beneficial for the development of the livestock industry.

SUPPLEMENTARY FILES

Supplementary File S1- Impact of antiseizure medications on cell viability.

Supplementary File S2- Therapeutic and toxic serum concentrations of antiseizure medications used in this study.

Supplementary File S3- List of primers used in this study: Primer sequence of genes used in the expression analysis.

Supplementary File S4- Effect of antiseizure drugs on the mRNA expression of DNA methyltransferases and DNA demethylases in HEK293.

Supplementary File S5– Effect of antiseizure drugs on the mRNA expression of One–carbon Metabolism genes in HEK293.

Supplementary File S6- Differentially methylated sites associated with Carbamazepine treatment: Listed are all the significant differentially methylated probes in AC vs. CBZ comparison.

Supplementary File S7- Differentially methylated sites associated with Phenytoin treatment: Listed are all the significant differentially methylated probes in AC vs. PHT comparison.

Supplementary File S8- Differentially methylated sites associated with Valproic acid treatment: Listed are all the significant differentially methylated probes in AC vs. VPA comparison.

Supplementary File S9- Differentially methylated sites associated with CBZ + Folic acid treatment: Listed are all the significant differentially methylated probes in AC vs. CF comparison.

Supplementary File S10- Differentially methylated sites associated with PHT + Folic acid treatment: Listed are all the significant differentially methylated probes in AC vs. PF comparison.

Supplementary File S11-Differentially methylated sites associated with VPA + Folic acid treatment: Listed are all the significant differentially methylated probes in AC vs. VF comparison.

Supplementary File S12- Differentially methylated sites associated with Folic acid treatment: Listed are all the significant differentially methylated probes in AC vs. FA comparison.

Supplementary File S13 -Summary of data filtering and protein identification across different treatment conditions.

Supplementary File S14- Differentially expressed proteins in Assay Control vs Carbamazepine treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. CBZ comparison.

Supplementary File S15- Differentially expressed proteins in Assay Control vs Phenytoin treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. PHT comparison

Supplementary File S16- Differentially expressed proteins in Assay Control vs valproic acid treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. VPA comparison

Supplementary File S17- Differentially expressed proteins in Assay Control vs Carbamazepine + Folic acid (CF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. CF comparison.

Supplementary File S18- Differentially expressed proteins in Assay Control vs phenytoin + Folic acid (PF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. PF comparison.

Supplementary File S19- Differentially expressed proteins in Assay Control vs Valproic acid + Folic acid (VF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. VF comparison.

Supplementary File S20- Differentially expressed proteins in Assay Control vs Folic acid (FA) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in AC vs. FA comparison

Supplementary File S21-Differentially expressed proteins in Carbamazepine vs Carbamazepine + Folic acid (CF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in C vs. CF comparison

Supplementary File S22- Differentially expressed proteins in phenytoin vs phenytoin + Folic acid (PF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in P vs. PF comparison

Supplementary File S23-Differentially expressed proteins in valproic acid vs valproic acid + Folic acid (VF) treated group: Listed are all the significant differentially expressed proteins (FDR<0.05) in V vs. VF comparison

Supplementary File S24 -Functional Enrichment analysis of differentially methylated probes associated with Carbamazepine treatment: Listed are all the significant Gene Ontology terms (FDR<0.05).

Supplementary File S25 -Functional Enrichment analysis of differentially methylated probes associated with Phenytoin treatment: Listed are all the significant Gene Ontology terms (FDR<0.05).

Supplementary File S26 - Functional Enrichment analysis of differentially methylated probes associated with Valproic acid treatment: Listed are all the significant Gene Ontology terms (FDR<0.05).

Supplementary File S27- Functional Enrichment analysis of upregulated proteins in drug alone treatment groups: Listed are all the significant Gene Ontology terms (FDR<0.05)

Supplementary File S28- Functional Enrichment analysis of downregulated proteins in drug alone treatment groups: Listed are all the significant Gene Ontology terms (FDR<0.05)

Per-fold AUC accuracy of FS-PLS vs Stagewise-FS on binary datasets.

Sensitivity, Specificity, and AUC accuracy per class of FS-PLS vs StepAIC on Álvez dataset.

The cortical cytoskeleton comprises mainly of the spectrin family of proteins. Spectrin and fodrin are members of this family. They support the plasma membrane. Fodrin is expressed in most tissues apart from the erythrocytes. Fodrin has been shown to be important in a wide variety of functions such as signal transduction, cardiac and brain development, etc. Through our studies it was understood that fodrin not only serves as a cortical supporting protein, but it also interacts with γ-tubulin and inhibits its microtubule nucleation activity. To gain a comprehensive view on the diverse functions of fodrin it was imperative to look into the downstream effectors of this protein in an unbiased fashion. Hence, we decided to downregulate α-fodrin in glioblastoma cells U-251 MG, and then perform a global protein expression analysis.

Sensitivity, Specificity, and AUC accuracy per class of FS-PLS vs StepAIC on RAPIDS dataset.

Comparative analysis of EHEC O157:H7 Sakai vs. TW14359 secreted and cytosolic protein using iTRAQ and mass spectrometry on an LTQ Orbitrap. Samples were fractionated through strong cation exchange and HPLC before analsyis. For TW14359 sepD secretome analysis, strains were similarly prepared and analyzed. All samples were searched against a TW14359 specific protein database (Uniprot) coupled with randomized protein sequences.

Within the GC6-74 cohorts, 4,462 HIV-negative healthy household contacts of 1,098 index TB cases were recruited from 2006 to 2012 at four African sites included in this study i.e. SUN (Stellenbosch University, South Africa), MRC (Medical Research Council Unit, The Gambia), AHRI (Armauer Hansen Research Institute, Ethiopia) and MAK (Makerere University, Uganda). The study had an exclusion period of 3 months, such that participants, who were diagnosed with active TB within 3 months of enrolment, were excluded from analysis to prevent inclusion of participants who had incipient or asymptomatic clinical TB disease at enrolment. Additional exclusion criteria were positive HIV rapid test, current or previous anti-retroviral treatment, history of TB, pregnancy, participation in drug and/or vaccine clinical trials and chronic disease diagnosis or immunosuppressive therapy within the past 6 months, and living in the study area for less than 3 months. A total of 97 individuals who developed active TB within the two year follow-up period were included in this study and matched at a ratio of 1:4 with participants who remained healthy during the 2-year follow-up period (controls); matching, by site, age class, sex, and wherever possible year of recruitment. Initial samples collected upon enrolment were termed baseline (BL) samples. Further samples were taken 6 and 18 months post-exposure, provided that the participant had remained TB free at the time of sample collection. Metabolic profiling was carried out for each study site, using either serum or plasma samples. For a small number of samples, an insufficient amount of plasma was available, so the sample was diluted using RPMI buffer.

Introduction: The standard of care for monitoring renal transplant patients include transplant biopsy and serum creatinine measurements. These are invasive procedures and are late and nonspecific markers of injury. Proteomics and proteins can provide biomarkers for monitoring pathology and become a useful tool in detection and treatment of conditions that occur after transplant. Areas covered: A survey of 273 studies in the biomarker detection field which included proteomics relating to renal pathology and normal controls was done. Analysis of this data showed pathways and biomarkers specific to different pathologies such as: AR, CR, IRI, TI, VR, DGF, AABMR, ACMR, immunosuppressant toxicity, and infection. It also revealed biomarkers proposed for better detection of these pathologies and the strength (sensitivity and specificity) of such biomarkers. Finally, the field of proteomics in renal transplant in terms of its methodologies, current challenges in clinical translation, and possible solutions are also discussed. Expert commentary: An analysis of biomarkers of acute and chronic rejection revealed acute rejection may be a more inflammatory process. A comparison of proteomic and protein based (n = 183), transcriptomic (n = 1), and genomic (n = 4) studies revealed that proteomic and protein based approaches may offer a clearer picture of inflammation in acute rejection.

Additional file 2: Supplemental Data 1. Table S1a. List of up-regulated genes in the WT_D vs WT_C comparison (log2FC ≥ 1, adjusted P value ≤ 0.01). Table S1b. List of down-regulated genes in the WT_D vs WT_C comparison (log2FC ≤ -1, adjusted P value ≤ 0.01). Table S1c. List of up-regulated genes in the hvd14_D vs hvd14_C comparison (log2FC ≥ 1, adjusted P value ≤ 0.01). Table S1d. List of down-regulated genes in the hvd14_D vs hvd14_C comparison (log2FC ≤ -1, adjusted P value ≤ 0.01). Table S1e. List of genes up-regulated by drought, only in WT (log2FC ≥ 1, adjusted P value ≤ 0.01) - WT Drought Resposne Specific (WT DRS) genes. Table S1f. List of genes down-regulated by drought, only in WT (log2FC ≤ -1, adjusted P value ≤ 0.01) - WT Drought Resposne Specific (WT DRS) genes. Table S1g. List of genes up-regulated by drought, only in hvd14 (log2FC ≥ 1, adjusted P value ≤ 0.01) - hvd14 Drought Resposne Specific (hvd14 DRS) genes. Table S1h. List of genes down-regulated by drought, only in hvd14 (log2FC ≤ -1, adjusted P value ≤ 0.01) - hvd14 Drought Resposne Specific (hvd14 DRS) genes. Table S1i. List of genes up-regulated by drought, in both genotypes: WT and hvd14 (log2FC ≤ -1, adjusted P value ≤ 0.01). Table S1j. List of genes down-regulated by drought, in both genotypes: WT and hvd14 (log2FC ≤ -1, adjusted P value ≤ 0.01).

Supplementary TablesTable S1. Genomes used for ISCompare evaluationTable S2. SurroundingLen parameter optimization. Sheet 1, results of the comparison of E. coli K-12 substr. MG1655 with an artificial genome of the same strain containing 100 IS30 random insertions. Sheet 2, results of the comparison using E. meliloti strain 1021 as reference genome and an artificial E. meliloti 2011 genome with 100 ISRm5 random insertions as query. Sheet 3, Sensitivity and precision analysis. TP, true positives; FP, false positives; FN, false negatives. * indicate values manually found by inspecting the VD reports.Table S3. ISCompare evaluation using 3,000 random IS30 insertions. Sheet 1: Statistical analysis. Sheet 2: Location of 3,000 randomly inserted IS30. Set 1. Sheet 3: ISCompare results using a compilation of ISs from ISFinder database and the randomly inserted IS30 set 1. Sheet 4: Location of 3,000 randomly inserted IS30. Set 2. Sheet 5: ISCompare results using IS30 as query IS and the randomly inserted IS30 set 2. TP, true positives; FP, false positives; FN, false negatives. * indicate values manually found by inspecting the VD reports.Table S4. Analysis of differentially located ISs on P. aeruginosa strains. Sheet 1: ISCompare result analysis. TP, true positives; FP, false positives; FN, false negatives. * indicate values manually found by inspecting the VD reports. TP-ALL and FP-ALL indicate the total number of TP or FP detected. Sheet 2: ANIb and DDH results.Table S5. Analysis of differentially located ISs on E. meliloti strains. Sheet 1: ISCompare results summary. TP, true positives; FP, false positives; FN, false negatives. * indicate values manually found by inspecting the VD reports. TP-ALL and FP-ALL indicate the total number of TP or FP detected. Sheet 2: ANIb calculated using ANI matrix calculator server. Sheet 3: dDDH results from http://ggdc.dsmz.de/ggdc.php.Table S6. Comparison of ISCompare results using the normal vs the Shift mode (-S). A comparison of DLIS in E. meliloti 1021 and GR4 strains was done using ISCompare with -S option set to 5,000 nucleotides. Green shading indicates new DLIS found in the -S mode. Red shading indicates TP DLIS that were incorrectly detected by the -S mode.Table S7. Comparison of ISCompare and ISSeeker using E. meliloti genomes. E. meliloti 1021 was used as reference, and compared to GR4 and U1022 strains as queries. In the case of GR4 strain, the -S option was also evaluated. As ISSeeker only can analyse one IS at a time, only ISRm2011-2 location was analyzed using both programs. Sheet 1: ISCompare vs ISSeeker results summary. Sheet 2: ISSeeker, 1021 vs GR4 results. Sheet 3: ISCompare, 1021 vs GR4 results. Sheet 4: ISCompare, 1021 vs GR4 results with -S 5000 setting. Sheet 5: ISSeeker, 1021 vs USDA1022 results. Sheet 6: ISCompare, 1021 vs USDA1022 results. Sheet 7: ISCompare, 1021 vs USDA1022 results with -S 5000 setting.Table S8. Comparison of ISCompare and ISSeeker using P. aeruginosa genomes. Sheet 1: ISCompare vs ISSeeker results summary. Sheet 2: ISSeeker results for all the analyzed P. aeruginosa strains. Sheet 3: results for all the analyzed P. aeruginosa strains.Table S9. Comparison of B. pertussis TOHAMA I with strains I127, J299 and J412 containing a IS481 insertion on the pertactin autotransporter gene. Sheet 1: Summary. Sheet 2: Results of TOHAMA I vs I127 using ISCompare. Sheet 3: Results of TOHAMA I vs J299 using ISCompare. Sheet 4: Results of TOHAMA I vs J412 using ISCompare.Supplementary filesFile S1. ContigBlastHit.pm. Modified ContigBlastHit.pm python module from ISSeeker.Supplementary Figure legendsFigure S1. E. meliloti Average Nucleotide Identity (ANIb) matrix and UPGMA distance tree. The ANIb matrix and UPGMA distance tree were calculated using the ANI-matrix calculator at Kostas lab server (http://enve-omics.ce.gatech.edu/g-matrix/). The accession numbers of the E. meliloti genomes used are listed on Table S1.Figure S2. Sensitivity and precision of ISCompare using different SurroundingLen values. SurroundingLen parameter optimization was evaluated using a range of nucleotide lengths between 100 and 2000. E. coli K-12 substr. MG1655 genome was compared with an artificial genome of the same strain containing 100 IS30 random insertions. In the case of E. meliloti, the comparison was done between strain 1021 as reference genome and an artificial E. meliloti 2011 genome with 100 ISRm5 random insertions as query. DLIS: Differentially located ISs; VD, discarded cases or cases tagged for manual verification. Figure S3. Phylogenetic tree of all the sequenced E. meliloti strains at NCBI genomes database. The phylogenetic tree was downloaded from NCBI genomes database. E. meliloti strains were selected according to their phylogenetic distance to the reference strain 1021. Selected strains are shown in color, from green for nearly related strains, to purple for more distant strains. The phylogenetic tree was edited using ITOL server (Letunic and Bork, 2019, https://itol.embl.de/).Figure S4. Phylogenetic tree of all the sequenced P. aeruginosa strains at NCBI genomes database. The phylogenetic tree was downloaded from NCBI genomes database. P. aeruginosa strains were selected according to their phylogenetic distance to the reference strain PAO1. Selected strains are shown in bold fonts. Collapsed branches are displayed as triangles. Leaves are shown as dots. The phylogenetic tree image was manually edited with Inkscape.Figure S5. Schematic representation of the possible cases resulting in DLIS, SLIS, and VD reports. Panes A-E represent cases which would be correctly identified by ISCompare as DLIS. Panes F-I correspond to cases which will be reported only using the -rs (report SLIS) option. Panes J-N are cases reported for manual verification, most of them involving repeated sequences. Some of these cases could be DLIS. Panes O-S are cases discarded from the analysis due to non significant or multiple blastn hits. Panes T-V, are other particular cases which could produce false positives. The query and reference genomes are represented by thick lines (green for query, and red/orange/yellow for reference) and ISs are represented as black boxes. Grey boxes represent different ISs and other genes. ltrA: group II intron-encoded protein LtrA. Colored circles indicate the category of differentially located IS candidate (Green, DLIS; Black, SLIS; Purple; magenta and dark blue are “Verify manually” categories; Yellow, orange and red are “Discarded from the analysis” categories).Figure S6. ISCompare shift mode. A. Schematic representation of the algorithm variations in the -S shit mode. B. Example of usage of -S mode for the identification of differentially located ISs flanking Group II introns. C. Example of usage of -S mode for the identification of differentially located Group II introns using ltrA gene.

BackgroundNon-coding RNAs (i.e., miRNAs) play a role in the development of obesity and related comorbidities and the regulation of body weight.ObjectiveTo identify candidate miRNA biomarkers throughout omics approaches in order to predict the response to specific weight-loss dietary treatments.DesignGenomic DNA and cDNA isolated from white blood cells of a subset from the RESMENA nutritional intervention study (Low-responders (LR) vs High-responders (HR)) was hybridized in Infinium Human Methylation450 BeadChip and in Illumina Human HT-12 v4 gene expression BeadChips arrays respectively. A bioinformatic prediction of putative target sites of selected miRNAs was performed by applying miRBase algorithms. HEK-293T cells were co-transfected with expression vectors containing the 3’-UTR of candidate genes to validate the binding of miRNAs to its target sites.Results134 miRNAs were differentially methylated between HR and LR in the methylation array, whereas 44 miRNAs were differentially expressed between both groups in the expression array. Specifically, miR-1237, miR-1976, miR-642, miR-636, miR-612 and miR-193B were simultaneously hypomethylated and overexpressed in HR. miR-612 and miR-1976 showed greatest differences in methylation and expression levels, respectively. The bioinformatic prediction revealed that TP53 was a putative target gene of miR-612 and CD40 of miR-1976. Moreover, TP53 was downregulated in the expression array when comparing HR vs LR expression levels adjusted by sex, diet, age and baseline weight, and CD40 showed a statistical trend. Furthermore, gene expression levels of TP53 and CD40 in white blood cells, when measured by qPCR, were also downregulated in HR. Finally, miR-612 and miR-1976 potently repressed TP53 and CD40 respectively by targeting its 3’-UTR regions.ConclusionmiR-612 and miR-1976 levels could be prospective biomarkers of response to specific weight-loss diets and might regulate the gene expression of TP53 and CD40.