Distant Reader - biochemistry-from-biorxiv

Introduction

This is a Distant Reader "study carrel", a set of structured data intended to help the student, researcher, or scholar use & understand a corpus.

This study carrel was created on 2021-01-10 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader biorxiv process, and the input was a Biorxiv bibliographic metadata (XML) file locally named input-file.xml.

Given the metadata file, the Reader downloaded each corresponding PDF file, and this resulted in a cache of preprints. Each of the preprints was then transformed & saved as a set of plain text files. All of the analysis -- "reading" -- has been done against these plain text files. For example, a short narrative report has been created. This Web page is a more verbose version of that report.

All study carrels are self-contained -- no Internet connection is necessary to use them. Download this carrel for offline reading. The carrel is made up of many subdirectories and data files. The manifest describes each one in greater detail.

Size

There are 50 item(s) in this carrel, and this carrel is 382,518 words long. Each item in your study carrel is, on average, 11,250 words long. If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following charts illustrate the overall size of the carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, the documents have an average readability score of 62. Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following charts illustrate the overall readability of the carrel.

Word Frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of the carrel's "aboutness". Excluding "stop words", some of the more frequent words include:

doi, org, https, biorxiv, preprint, al, et, nd, version, author, review, holder, peer, copyright, funder, posted, certified, january, preprintthis, nc, using, figure, cell, protein, cells, http, display, cc, fig, data, international, license, licenses, granted, creativecommons, perpetuity, mm, analysis, binding, made, used, il, licenseavailable, dna, structure, proteins, two, complex, time, available

Using the three most frequent words, the three files containing all of those words the most are High-throughput tandem-microwell assay for ammonia repositions FDA-Approved drugs to Helicobacter pylori infection, Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders, and Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth.

The most frequent two-word phrases (bigrams) include:

et al, peer review, biorxiv preprint, author funder, version posted, copyright holder, preprintthis version, posted january, preprint https, org licenses, granted biorxiv, international licenseavailable, international licensemade, licensemade available, rights reserved, without permission, allowed without, reuse allowed, cell lines, amino acids, disulfide bond, mm nacl, hbv integration, crystal structure, active site, amino acid, mass spectrometry, outer membrane, editing frequency, nucleic acids, fatty acid, pi kc, fatty acids, lipid species, mm tris, mg ml, supplementary table, ulk complex, ps ta, ms ms, pc pc, au content, biol chem, saccharomyces cerevisiae, signal peptide, extended data, lc lipidation, rna editing, ps ps, spike protein

And the three file that use all of the three most frequent phrases are Structural basis for broad coronavirus neutralization Intramolecular quality control: HIV-1 Envelope gp160 signal-peptide cleavage as a functional folding checkpoint, and Priming mycobacterial ESX-secreted protein B to form a channel-like structure.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

Keywords

Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in the carrel include:

january, international, fig, figure, sars, dna, ace2, supp, rna, rbd, cell, aβ40, 𝐷27, 𝐷23, −)-gr24, wärmländer, wang, vpx, vprmus, vitiligo, ulk1, uclust, type, trypanosoma, tmd, table, synuclein, supplemental, suppl, std, stat1, snm1a, scornet, samhd1, rpe1, rpe, roc1, rnc, rbd-62, protein, prescription, preprint, pre, pqa, ppe, pos, pnpla2, pi3kc3-c1, pedf, pdi

And now word clouds really begin to shine:

Topic Modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If the study carrel could be summed up in a single word, then that word might be 10, and Auto-CORPus: Automated and Consistent Outputs from Research Publications is most about that word.

If the study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:

If the study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

10, org, et - Intramolecular quality control: HIV-1 Envelope gp160 signal-peptide cleavage as a functional folding checkpoint
10, org, 2021 - Evaluating the transcriptional fidelity of cancer models
10, il, doi - Competitive binding of STATs to receptor phospho-Tyr motifs accounts for altered cytokine responses in autoimmune disorders
org, 2021, 10 - Engineering the thermotolerant industrial yeast Kluyveromyces marxianus for anaerobic growth
org, 10, https - Insights into Genome Recoding from the Mechanism of a Classic +1-Frameshifting tRNA

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Noun & Verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns helps you answer what questions; "What is discussed in this collection?":

preprint, version, author, review, holder, peer, copyright, funder, preprintthis, cells, license, perpetuity, %, protein, data, cell, fig, analysis, licenseavailable, figure, structure, proteins, p, c, model, a., time, activity, site, b, membrane, licensemade, sequence, complex, dna, rights, reuse, permission, h, e, gene, study, min, expression, cancer, -, results, domain, genes, a

An enumeration of the verbs helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:

is, was, were, are, has, posted, certified, using, display, biorxiv, granted, be, made, used, binding, have, based, allowed, shown, performed, reserved, been, containing, found, bound, described, incubated, observed, showed, identified, associated, obtained, compared, determined, including, indicated, induced, calculated, measured, reported, followed, purified, increased, added, derived, show, following, showing, selected, analyzed

Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

al, january, et, c, j., ., m., s., nd, m, s, nc, international, ⋅, c., d., a., j, r., figure, l., k., e., h., e, mm, p., b., a, fig, b, il-27, g., t., ph, d, n, biol, −, sars, f, p, y., g, w., l, n., sp, cov-2, µm

An analysis of personal pronouns enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?"

we, it, our, its, their, they, i, his, them, us, https://doi.org/10.1101/2021.01.05.425432, itself, ya, he, one, https://doi.org/10.1101/2021.01.08.425918, https://doi.org/10.1101/2020.06.17.156679, https://doi.org/10.1101/2020.04.24.059154, u, me, em, you, themselves, il-27ra, −)-gr24, zncl2, s, my, mtorc1, matchdrugwithdisease, ico, https://doi.org/10.1101/2021.01.08.425875, https://doi.org/10.1101/2021.01.08.423993, +1-frameshifting, 𝑓, †, biochim, ~1:50, ~125, your, us-, snm1b, rcsfgs2, pskai2a, pad2bsabi, oxysterols, ourself, ng, na, mrnas

Below are words cloud of your study carrel's proper & personal pronouns.

Adjectives & Verbs

Learning about a corpus's adjectives and adverbs helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"

-, available, different, international, human, high, other, molecular, single, structural, specific, non, same, such, dependent, biological, similar, new, multiple, free, first, relative, higher, low, active, final, cryo, significant, more, complex, additional, anti, disulfide, amino, independent, functional, standard, native, several, preprint, full, important, small, distinct, initial, cellular, large, negative, viral, positive

not, also, then, well, only, however, respectively, previously, more, as, thus, here, further, out, most, together, highly, therefore, significantly, first, even, assays, above, very, directly, up, next, prior, finally, down, likely, interestingly, recently, m, twice, moreover, furthermore, at, least, subsequently, alone, thereby, still, specifically, e.g., approximately, additionally, notably, much, left

Next steps

There is much more to a study carrel than the things outlined above. Use this page's menubar to navigate and explore in more detail. There you will find additional features & functions including: ngrams, parts-of-speech, grammars, named entities, topic modeling, a simple search interface, etc.

Again, study carrels are self-contained. Download this carrel for offline viewing and use.

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