Additional file 1 of Genes with human-specific features are primarily involved with brain, immune and metabolic evolution
Creators: name: Mainá Bitar
name: Kuiper, Stefanie
name: O’Brien, Elizabeth
name: Barry, Guy
Publisher: name: figshare
Year: 2020
Subjects: subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: 111714 Mental Health
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Hematology
subject: 110309 Infectious Diseases
subject: Plant Biology
subject: Computational Biology

description: Table S1. The screenshot above represents the first lines of the table. The full version is given as an independent Supplementary Table in xls format. This file contains all 845 genes with humanspecific features retrieved in this study (Sheet 1 - "HumanSpecific genes") and describes for each gene its: (A) Gene name (updated to the current Ensembl description, when necessary); (B) Ensembl ID; (C) Chromosome number; (D) Gene type (the specific type, as described by Ensembl); (E) General gene type (a general classification which may group multiple gene types - e.g. pseudogenes includes processed, unprocessed and transcribed pseudogenes); (F) Mechanism of origin (specifically as described by the author of the correspondent reference); (G) General mechanism of origin (a manually assigned general classification which may group multiple subclasses from column (F) - data from this column was used to generate the pie chart presented in Figure 1a in the main manuscript) and (H) At least one reference in which the gene is reported (the full list of references, numbered accordingly, is given as Sheet 2 - "References"). The file also contains information on 19 large gene families (Sheet 3 - "HumanSpecific GeneFamilies"), described as undergoing significant expansion or accelerated evolution across all (or many of) its members. These were not included in the main table, mainly to prevent their high gene numbers to introduce a functional bias in the dataset. Table S2. This table presents the percentage of protein-coding genes in each chromosome (A), both for the set of genes with human-specific features (B) and the entire set of human proteins retrieved from the Ensembl database (C). A p-value is given (D), generated with a Fisher's exact test to represent the significance of the difference between (B) and (C) per chromosome. Chromosomes X and 7 are clearly enriched in genes with human-specific features and another four (in green) have significantly more of such genes than expected. Table S3. This table describes the metagroups generated by GeneTerm Linker using FGNet. The metagroup number corresponds to numbers in Supplementary Figures 1A and 1B (and information can be transferred to Figure 1B in the manuscript). For each metagroup this file presents its silhouette size (a clustering coefficient), significance (p-value), number of constituent genes and constituent functional terms (or, for metagroups 1 and 6, which do not meet inclusion cutoffs, their exclusion criteria). The last column on the right describes functional terms in each metagroup and their annotation space, which can be a gene ontology assignment (GO for biological process, molecular function or cellular component), a KEGG pathway or a function inferred from the description of InterPro motifs or domains (IPR).Table S4. This table presents gene expression levels (in FPKM) for transcripts related to 61 humanspecific genes which were characterized as highly expressed in at least one subpopulation of glial cell (sequencing data retrieved from SRA). Highly expressed transcripts were defined as the top ~10% of the expressed transcripts (i.e. the 2,000 transcripts) with highest average FPKM values for each set of samples under investigation. On average ~1580 genes were characterized as highly expressed in each cell type and compared with the set of 856 genes with human-specific features. We retrieved 23 highly expressed human-specific genes from the radial glial cell samples, 17 from the outer radial glial cell samples, 26 from the progenitor cells samples and 24 from the neuron cell samples, resulting in a set of 61 nonredundant genes and 91 transcripts. Table S5. Primers designed for each of the 12 genes described in the Figure 4 of the main manuscript. Gene names and accession numbers are also provided.Figure S1. These functional networks describe the set of genes with human-specific features. The network outputs were generated with FGNet to represent the metagroups defined with GeneTerm Linker. Individual files are provided to allow assessment of gene names and network topology. A) From a total of 25 metagroups (clusters of associated genes with coherent biological significance), 2 were filtered out for not meeting the parameters for measuring relevance, in terms of significance and coherence. The remaining 23 metagroups represent 225 genes and many different functions, including neuronal, metabolic and immunological. Metagroups are color-coded and their full description is given in the Supplementary Table 3. Gene names and network topology can be better visualized when magnified. White circles denote genes shared by multiple metagroups. Figure S2. The Ingenuity Pathway Analysis (IPA) is an additional tool for functional analysis of highthroughput sequencing data. In this figure we present results generated using IPA for the set of genes with human-specific features. This result includes &gt;85% of the 845 genes in the dataset and describes these in terms of the pathways in which they function. A) The plot presents category scores. The "threshold" line (vertical line in light orange, set here to 1.25) indicates the minimum significance level in terms of inverse logarithmic p values [-log(p-value)] derived from performing a Fisher’s exact test. The proportion of genes in the dataset that map to each pathway in the IPA knowledgebase is represented as the "ratio" (line in darker orange). The z-score is color-coded and refers to the difference between observed and predicted up/down regulation states of pathways. B) The table shows higher-order functional classes with their respective significance (p values) and the number of genes (molecules) by which they are composed. (PDF 2947 kb)