GapMind for catabolism of small carbon sources


ethanol catabolism

Analysis of pathway ethanol in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 etoh-dh-nad, adh, acs
Azospirillum brasilense Sp245 etoh-dh-nad, adh, acs
Bacteroides thetaiotaomicron VPI-5482 etoh-dh-nad, adh, ackA, pta
Burkholderia phytofirmans PsJN etoh-dh-nad, adh, ackA, pta
Caulobacter crescentus NA1000 etoh-dh-nad, adh, acs
Cupriavidus basilensis 4G11 etoh-dh-nad, adh, ackA, pta
Dechlorosoma suillum PS etoh-dh-nad, adh, acs
Desulfovibrio vulgaris Hildenborough etoh-dh-nad, adh, ackA, pta
Desulfovibrio vulgaris Miyazaki F etoh-dh-nad, ald-dh-CoA
Dinoroseobacter shibae DFL-12 etoh-dh-nad, adh, ackA, pta
Dyella japonica UNC79MFTsu3.2 etoh-dh-nad, adh, acs
Echinicola vietnamensis KMM 6221, DSM 17526 etoh-dh-nad, adh, acs
Escherichia coli BW25113 etoh-dh-nad, adh, ackA, pta
Herbaspirillum seropedicae SmR1 etoh-dh-nad, adh, acs
Klebsiella michiganensis M5al etoh-dh-nad, adh, ackA, pta
Magnetospirillum magneticum AMB-1 etoh-dh-nad, adh, acs
Marinobacter adhaerens HP15 etoh-dh-nad, adh, ackA, pta
Paraburkholderia bryophila 376MFSha3.1 etoh-dh-nad, adh, acs
Pedobacter sp. GW460-11-11-14-LB5 etoh-dh-nad, adh, ackA, pta
Phaeobacter inhibens BS107 etoh-dh-nad, adh, acs
Pseudomonas fluorescens FW300-N1B4 etoh-dh-nad, adh, ackA, pta
Pseudomonas fluorescens FW300-N2C3 etoh-dh-nad, adh, ackA, pta
Pseudomonas fluorescens FW300-N2E2 etoh-dh-nad, adh, ackA, pta
Pseudomonas fluorescens FW300-N2E3 etoh-dh-nad, adh, ackA, pta
Pseudomonas fluorescens GW456-L13 etoh-dh-nad, adh, ackA, pta
Pseudomonas putida KT2440 etoh-dh-nad, adh, ackA, pta
Pseudomonas simiae WCS417 etoh-dh-nad, adh, ackA, pta
Pseudomonas stutzeri RCH2 etoh-dh-nad, adh, ackA, pta
Shewanella amazonensis SB2B etoh-dh-nad, adh, ackA, pta
Shewanella loihica PV-4 etoh-dh-nad, adh, ackA, pta
Shewanella oneidensis MR-1 etoh-dh-nad, adh, ackA, pta
Shewanella sp. ANA-3 etoh-dh-nad, adh, ackA, pta
Sinorhizobium meliloti 1021 etoh-dh-nad, adh, ackA, pta
Sphingomonas koreensis DSMZ 15582 etoh-dh-nad, adh, acs
Synechococcus elongatus PCC 7942 etoh-dh-nad, adh, acs

Confidence: high confidence medium confidence low confidence
transporter – transporters and PTS systems are shaded because predicting their specificity is particularly challenging.

This GapMind analysis is from Sep 17 2021. The underlying query database was built on Sep 17 2021.



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About GapMind

Each pathway is defined by a set of rules based on individual steps or genes. Candidates for each step are identified by using ublast (a fast alternative to protein BLAST) against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer with enzyme models (usually from TIGRFam). Ublast hits may be split across two different proteins.

A candidate for a step is "high confidence" if either:

where "other" refers to the best ublast hit to a sequence that is not annotated as performing this step (and is not "ignored").

Otherwise, a candidate is "medium confidence" if either:

Other blast hits with at least 50% coverage are "low confidence."

Steps with no high- or medium-confidence candidates may be considered "gaps." For the typical bacterium that can make all 20 amino acids, there are 1-2 gaps in amino acid biosynthesis pathways. For diverse bacteria and archaea that can utilize a carbon source, there is a complete high-confidence catabolic pathway (including a transporter) just 38% of the time, and there is a complete medium-confidence pathway 63% of the time. Gaps may be due to:

GapMind relies on the predicted proteins in the genome and does not search the six-frame translation. In most cases, you can search the six-frame translation by clicking on links to Curated BLAST for each step definition (in the per-step page).

For more information, see:

If you notice any errors or omissions in the step descriptions, or any questionable results, please let us know

by Morgan Price, Arkin group, Lawrence Berkeley National Laboratory