Transcription Statistics 2024 – Everything You Need to Know

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Are you looking to add Transcription to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Transcription statistics of 2024.

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Best Transcription Statistics

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Transcription Market Statistics

  • Approximately 64 percent of surveyed experts within the industries of Elearning and market research used speechto text automated transcription in 2020. [0]

Transcription Latest Statistics

  • Employment of medical transcriptionists is projected to decline 7 percent from 2020 to 2030. [1]
  • the FDM is highly correlated with JSD* when average RNA seq coverage of the transcripts is sufficiently deep; and the FDM is able to identify 90% of genes with differential transcription when JSD* >0.28 and coverage. [2]
  • This represents higher sensitivity than Cufflinks and rDiff , which respectively identified 69 and 49% of the genes in this region as differential transcribed. [2]
  • Using annotations identifying the transcripts, Cufflinks was able to identify 86% of the genes in this region as differentially transcribed. [2]
  • For example, a gene with two transcripts T1 and T2 and a transcript expression vector of [p1, p2] indicates that p1% of transcripts are T1 and. [2]
  • With coverage of 20 or higher, 90 % of true positives can be identified with ~10% false positives. [2]
  • Figure 4 shows that with high coverage, 90% of true positives can be identified with ~10% of false positives. [2]
  • The method finds 90% of the genes which have JSD* >0.28 and coverage >7 as significant. [2]
  • >7, FDM was able to identify 90% of the genes as differentially transcribed. [2]
  • This represents higher sensitivity than Cuffdiff and rDiff , which identified differential transcription between 68% and 49% of the genes in this region, respectively. [2]
  • For comparison, we also ran Cuffdiff with gene annotations, which identified differential transcription in 86% of the genes in this region. [2]
  • For example, for genes with JSD* >0.28 and log>0.85 ( >7). [2]
  • , had lower sensitivity, identifying differential transcription in 68 and 49% of the genes in this region, respectively. [2]
  • Working from the ACT Graphs, an average fold change of 25 was predicted. [2]
  • A third gene ZNF408 gave a different result in the biological experiment than predicted by the FDM method. [2]
  • For genes selectively controlled by IL6, regulation was profoundly affected by the absence of STAT3 (>90%); a much smaller proportion (20.3%). [3]
  • For genes commonly regulated by IL6 and IL 27, the influence of STAT3 was still dominant (74.9%) over that of STAT1 (20.6%). [3]
  • For genes selectively regulated by IL 27, the contribution of STAT1 was more prominent (65.0%), although the influence of STAT3 was still profound (76.8%). [3]
  • strictly STAT1dependent and 79 genes (25.4%). [3]
  • In the absence of STAT1, however, the genes regulated by IL6 or IL 27 largely overlapped (726 genes of 811 genes; 89.5%). [3]
  • The three main principal components in the model contribute to explain 30.7%, 14.5%, and 9.13% of the variation, respectively, and are predictive. [3]
  • To quantify the likelihood of homoand hetero dimer binding, we calculated the ratio of STAT1 and STAT3 signals at peak locations and segregated them into three bins according to the three possible STAT1 and STAT3 configurations. [3]
  • Of these, 33% of STAT3 peaks overlap with 56% of STAT1 peaks. [3]
  • Many of the genes affected by STAT1 GOF mutations were immunologically relevant, and thus likely contribute to the pathologies associated with this genetic mutation. [3]
  • Based on these findings, it might be predicted that in the absence of STAT3, IL6 would approximate the action of IL 27 through activation of STAT1. [3]
  • STAT3 occupies a wider region of chromatin than STAT1, often as homodimers and also likely in cooperation with STAT1 as heterodimers. [3]
  • Electrophoresis was performed on a 4% native polyacrylamide gel , and the radioactivity was visualized by autoradiography. [3]
  • Cells cultured as indicated were cross linked for 10 min with 1% formaldehyde and harvested. [3]
  • The membrane was blocked for 60 minutes at room temperature with 5% nonfat dry milk in TTBS , 100 mM NaCl, 0.1% Tween. [4]
  • After addition of 1 μl of 10× loading buffer the entire reaction mixture was run for 45 minutes on a 4% polyacrylamide gel with 0.5× TBE , 50 μM EDTA, 1.8 mM borate) at 300 V. [4]
  • On the other slides, tissue sections were made permeable with 0.1% Triton X. [4]
  • in 0.1 M PBS, washed three times in PBS, and blocked non specifically with 0.75% bovine serum albumin in PBS. [4]
  • Subsequently, sections were incubated for 60 minutes with the appropriate antibody diluted 1100 to 1200 in 0.75% bovine serum albumin. [4]
  • After washing in PBS, tissue bound antibody was detected using biotinylated goatantimouse IgG antibodies , followed by avidin FITC , both diluted at 1100 in 5% human serum. [4]
  • The probability is 100% for the performance test on the order of the background model, 50% for the tests of statistics across multiple thresholds and finally between 10–90% in the dilution test. [5]
  • Based on this we test all statistics with a sequence set with 50% chance of an embedded site with both zeroth order and third order background models. [5]
  • Finally, in the dilution test it is evident that this statistic is also relatively robust with respect to the number of sites in the positive set never dropping below a sensitivity of 50% as shown in table 3. [5]
  • 10% 20% 30% 40% 50% 60% 70% 80% 90% TRUE 61. [5]
  • Also, transcription factor activities have been estimated through their effect on target genes [23]. [6]
  • According to the definition provided in [34] the “master regulator” transcription factor is at the top of a regulatory hierarchy and must not be under the regulatory influence of any other gene or transcription factor. [6]
  • Therefore, according to the definition, TF1 can be considered as the master regulator transcription factor. [6]
  • The power curve starts from 3.2% at δ = 0. [6]
  • and reaches its maximum of 100% at δ = 1. [6]
  • The power reaches over 80% with a moderate choice of δ = 0.6. [6]
  • After adjusting for false discovery rate at 5% significance level, 6054 probes are differentially expressed in the two groups, out of which 542 are transcription factors. [6]
  • After adjusting for FDR at 10% significance level, the number of differentially expressed genes turns out to be 5192, among which 266 are transcription factors. [6]
  • According to [66], “NFKB” may contribute to the promotion of the ongoing inflammatory process in the gut mucosa resulting in the progression of colitis associated colorectal cancer. [6]
  • Cell were dissociated with purified collagenase for 30 min at 37°C in the presence of trypsin inhibitor, freed of fibroblasts by preplating, and cultured for 24 hr in medium with 5% horse serum and 5% fetal calf serum in Leibovitz’s medium. [7]
  • 2, 0.2 mM EDTA, 0.1% Triton X 100, 1 mM DTT, 1 mM phenylmethylsulfonyl fluoride, and leupeptin in a Wheaton Dounce homogenizer. [7]
  • After a 30 min incubation on ice, the reactions were analyzed by electrophoresis on 8% polyacrylamide gel in 0.375× TBE. [7]
  • Fifty microliters of 50% protein A agarose, prewashed in lysis buffer , was then added and the mixture was incubated for 2 hr at 4°C. [7]
  • Each sample was washed with washing buffer containing 150 mM NaCl, 50 mM Tris⋅HCl , 5 mM EDTA, 0.25% Triton X 100, 2 mM phenylmethylsulfonyl fluoride, aprotinin , 1 mM. [7]
  • After isolating the tyrosine phosphorylated protein by agaroseprotein A treatment, the proteins were separated in a 7% SDS/polyacrylamide gel and electroblotted on to nitrocellulose membrane. [7]
  • It is likely that those involved in activation and maintenance of local RAS via binding to ANG promoter are common STATs and the remaining target other responsive genes. [7]
  • Jurkat T cell lines were obtained from the and maintained in RPMI with 10% fetal bovine serum supplemented with penicillin and streptomycin. [8]
  • Beginning with the isoforms expressed at the highest frequency, isoforms were collected until the frequency of the collected isoforms surpassed 90% of the aggregate reads for the transcription unit. [8]
  • P = 10 and 30%, two levels of artificial noise proportions Q = 10 and 30% and two levels of sample sizes R = 3 and 5 replicate libraries. [8]
  • For small condition effect or low artificial noise proportion (Q = 10%) or low proportion (P = 10%). [8]
  • baySeq showed higher powers in low artificial noise proportion but it also had higher true FDRs than estimated FDRs in most cases. [8]
  • In high artificial noise proportion (Q = 30%). [8]
  • edgeR GLM showed high powers in all 12 given scenarios but its true FDRs were much larger than estimated in 9 scenarios , suggesting that this method is also not conservative. [8]
  • In low DE isoform proportion (P = 10%), low artificial noise proportion (Q = 10%). [8]
  • small condition effect scenario, edgeR Exact test performed poorly because its true FDRs were much larger than its estimated values in most cases, however, in high P(30%), high Q(30%). [8]
  • Similarly to DESeq, mBeta ttest also had lower true FDRs than its estimated values in all 12 given scenarios but it had much higher power than DESeq , showing that the mBeta t test method is conservative and powerful. [8]
  • Simulated data from scenario 1 (proportion of differentially expressed isoforms = 10%, technical noise proportion = 10%, treatment effect A = 100, and sample size = 3). [8]
  • This is because in the case of five replicate libraries, the two methods underestimated their FDR at cutoff α = 0.05 so that they lost conservativeness. [8]
  • In contrast, the edgeR GLM method identified 4376 (45%) genes and 5039 (37%). [8]
  • Highlighting its high degree of conservativeness and stringency, DESeq found only 261 (3%) differentially expressed genes and 287 (2%). [8]
  • In DE genes and DE isoforms , 98% of meta ttest findings are the same with eta ttest findings, the edgeR Exact test method has about 70% of findings that overlap with eta ttest and meta t. [8]
  • tvalues were obtained by the two Beta t test methods from the simulated data in which values τ = 100U were assigned to 10% of genes for differential expression between two given conditions each with three replicates. [8]
  • 0070 genes, 3 replicate libraries in each of two conditions, 0% of genes with technical noise and 0% of DE genes and difference effect value = 00U where U is uniform variable and. [8]
  • 11341 genes, 3 replicate libraries in each condition, 20% of genes with technical noise, 30% DE genes, and difference effect value =. [8]
  • In addition, DESeq2 was applied to our real gene transcriptomic data and found 1243 DE genes, of which 1023 DE genes (83%). [8]
  • Available to download in PNG, PDF, XLS format 33% off until Jun 30th. [0]
  • P. falciparumin synchronized in vitro culture , revealing a highly unusual pattern of gene expression in which >80% of genes are transcribed in a wavelike pattern, with a single maximum and a single minimum within the cell cycle. [9]
  • The maximum likelihood estimates and 95% confidence intervals for the samples in this study are shown. [9]
  • Between these 2 groups, we identified 278 genes, representing ≈5% of the genome, as differentially expressed [ P≤ 0.001, using the Benjamini Yekutieli multiple test correction ]. [9]
  • In all 3 cases, the model yielded estimates of gametocyte mRNA fraction, α, which were highly correlated with the measured percentage of gametocytes in the culture. [9]
  • Similarly, at least in the 3D7 asexual development dataset, the high rate at which this parasite commits to gametocytes means that contaminating early stages of gametocytes are likely to be present. [9]
  • The asexual genes also expressed in gametocytes are likely to be expressed under the same transcriptional control mechanisms as those expressed at or ≈30 HPI. [9]
  • Thisrue reference, μ g, is estimated using smoothing splines , yielding μ̂. [9]
  • The assumption of Gaussian noise closely conforms to previous biological findings , with σ ε 2estimated, using differences across the 3 clones measured by Llinás et al. [9]
  • time course consisting of 100% gametocytes are highly correlated, and results vary little using different sexual reference strains. [9]

I know you want to use Transcription Software, thus we made this list of best Transcription Software. We also wrote about how to learn Transcription Software and how to install Transcription Software. Recently we wrote how to uninstall Transcription Software for newbie users. Don’t forgot to check latest Transcription statistics of 2024.

Reference

  1. statista – https://www.statista.com/statistics/1133885/automated-transcription-usage-worldwide-by-industry-and-frequency/.
  2. bls – https://www.bls.gov/ooh/healthcare/medical-transcriptionists.htm.
  3. oup – https://academic.oup.com/bioinformatics/article/27/19/2633/231261.
  4. sciencedirect – https://www.sciencedirect.com/science/article/pii/S1074761315001740.
  5. bmj – https://gut.bmj.com/content/51/3/379.
  6. nih – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2229843/.
  7. biomedcentral – https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1499-x.
  8. pnas – https://www.pnas.org/doi/10.1073/pnas.95.10.5590.
  9. plos – https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0123658.
  10. pnas – https://www.pnas.org/content/106/18/7559.

How Useful is Transcription

One of the key benefits of transcription is its ability to provide a written record of spoken words. This is particularly valuable in professional settings, where meetings and conferences may need to be documented for future reference. By transcribing these events, individuals can easily refer back to the information discussed and ensure that nothing important is overlooked.

Transcription is also invaluable in the medical field, where accurate documentation is essential for patient care. Doctors and other healthcare professionals often rely on transcriptions of patient consultations, procedures, and treatments to ensure that all information is accurately recorded and easily accessible. Transcription services can help streamline this process, making it easier for healthcare providers to stay organized and provide the best possible care for their patients.

Similarly, transcription can be a valuable tool in academic research. Recording and transcribing interviews, focus groups, and other qualitative data can help researchers analyze and interpret their findings more accurately. Transcriptions can also aid in the writing process, making it easier to organize and reference key information in academic papers and articles.

Beyond professional and academic settings, transcription can also be useful for personal purposes. Capturing and transcribing conversations, lectures, or speeches can help individuals retain information and revisit key points later on. Transcription services can even be used to create written records of family stories, memories, or other important personal events, preserving them for future generations.

In addition to its practical applications, transcription also offers accessibility benefits for those with disabilities. For individuals who are deaf or hard of hearing, transcriptions can provide a way to access spoken information that they may not be able to hear. Likewise, transcriptions can be useful for individuals with visual impairments, providing a written alternative to audio or video content.

Overall, the usefulness of transcription services cannot be overstated. From businesses to healthcare providers to researchers, transcription offers a valuable tool for capturing and preserving spoken information in a written format. In a world where information is constantly being shared and communicated, transcription services play a crucial role in helping individuals and organizations stay organized, informed, and connected.

In Conclusion

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