GapMind for catabolism of small carbon sources


citrate catabolism

Analysis of pathway citrate in 35 genomes

Genome Best path
Acidovorax sp. GW101-3H11 tctA, tctB, tctC, acn, icd
Azospirillum brasilense Sp245 tctA, tctB, tctC, acn, icd
Bacteroides thetaiotaomicron VPI-5482 SLC13A5, acn, icd
Burkholderia phytofirmans PsJN citA, acn, icd
Caulobacter crescentus NA1000 SLC13A5, acn, icd
Cupriavidus basilensis 4G11 citA, acn, icd
Dechlorosoma suillum PS SLC13A5, acn, icd
Desulfovibrio vulgaris Hildenborough SLC13A5, acn, icd
Desulfovibrio vulgaris Miyazaki F SLC13A5, acn, icd
Dinoroseobacter shibae DFL-12 tctA, tctB, tctC, acn, icd
Dyella japonica UNC79MFTsu3.2 SLC13A5, acn, icd
Echinicola vietnamensis KMM 6221, DSM 17526 SLC13A5, acn, icd
Escherichia coli BW25113 fecB, fecC, fecD, fecE, citD, citE, citF
Herbaspirillum seropedicae SmR1 citA, acn, icd
Klebsiella michiganensis M5al fecB, fecC, fecD, fecE, citD, citE, citF
Magnetospirillum magneticum AMB-1 SLC13A5, acn, icd
Marinobacter adhaerens HP15 tctA, tctB, tctC, acn, icd
Paraburkholderia bryophila 376MFSha3.1 citA, acn, icd
Pedobacter sp. GW460-11-11-14-LB5 SLC13A5, acn, icd
Phaeobacter inhibens BS107 tctA, tctB, tctC, acn, icd
Pseudomonas fluorescens FW300-N1B4 citM, acn, icd
Pseudomonas fluorescens FW300-N2C3 tctA, tctB, tctC, acn, icd
Pseudomonas fluorescens FW300-N2E2 tctA, tctB, tctC, acn, icd
Pseudomonas fluorescens FW300-N2E3 citA, acn, icd
Pseudomonas fluorescens GW456-L13 citM, acn, icd
Pseudomonas putida KT2440 citM, acn, icd
Pseudomonas simiae WCS417 citA, acn, icd
Pseudomonas stutzeri RCH2 tctA, tctB, tctC, acn, icd
Shewanella amazonensis SB2B SLC13A5, acn, icd
Shewanella loihica PV-4 SLC13A5, acn, icd
Shewanella oneidensis MR-1 SLC13A5, acn, icd
Shewanella sp. ANA-3 SLC13A5, acn, icd
Sinorhizobium meliloti 1021 tctA, tctB, tctC, acn, icd
Sphingomonas koreensis DSMZ 15582 SLC13A5, acn, icd
Synechococcus elongatus PCC 7942 SLC13A5, acn, icd

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 the paper from 2019 on GapMind for amino acid biosynthesis, the paper from 2022 on GapMind for carbon sources, or view the source code.

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